DETAILED ACTION
The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA .
Notice for all US Patent Applications filed on or after March 16, 2013
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.
Status of the Claims
This communication is in response to communications received on 9/9/25. Claim(s) 1, 4, and 10-13 is/are amended, claim(s) 3 is/are cancelled, claim(s) none is/are new, and applicant does not provide any information on where support for the amendments can be found in the instant specification. Therefore, Claims 1-2 and 4-13 is/are pending and have been addressed below.
Response to Arguments
Applicant’s arguments, see applicant’s remarks, filed 9/9/25, with respect to rejections under 35 USC 112 for claim(s) 3-9 have been fully considered and are persuasive. The Examiner respectfully withdraws rejections under 35 USC 112 for claim(s) 3-9.
Applicant’s arguments, see applicant’s remarks, filed 9/9/25, with respect to rejections under 35 USC 101 for claim(s) 1-2 and 4-13 have been fully considered but they are not persuasive as far as they apply to the amended 101 rejection(s).
Applicant’s arguments, see applicant’s remarks, filed 9/9/25, with respect to rejections under 35 USC 103 for claim(s) 1-2 and 4-13 have been fully considered but they are not persuasive as far as they apply to the amended 103 rejection(s).
Claim Rejections - 35 USC § 112
The following is a quotation of the first paragraph of 35 U.S.C. 112(a):
(a) IN GENERAL.—The specification shall contain a written description of the invention, and of the manner and process of making and using it, in such full, clear, concise, and exact terms as to enable any person skilled in the art to which it pertains, or with which it is most nearly connected, to make and use the same, and shall set forth the best mode contemplated by the inventor or joint inventor of carrying out the invention.
The following is a quotation of the first paragraph of pre-AIA 35 U.S.C. 112:
The specification shall contain a written description of the invention, and of the manner and process of making and using it, in such full, clear, concise, and exact terms as to enable any person skilled in the art to which it pertains, or with which it is most nearly connected, to make and use the same, and shall set forth the best mode contemplated by the inventor of carrying out his invention.
Generic claim language in the original disclosure does not satisfy the written description requirement if it fails to support the scope of the genus claimed. Ariad, 598 F.3d at 1350, 94 USPQ2d at 1171; Enzo Biochem, Inc. v. Gen-Probe, Inc., 323 F.3d 956, 968, 63 USPQ2d 1609, 1616 (Fed. Cir. 2002) (holding that generic claim language appearing in ipsis verbis in the original specification did not satisfy the written description requirement because it failed to support the scope of the genus claimed)”. Additionally, original claims may fail to satisfy the written description requirement when the invention is claimed and described in functional language but the specification does not sufficiently identify how the invention achieves the claimed function. Ariad, 598 F.3d at 1349, 94 USPQ2d at 1171.
Claim(s) 1-2 and 4-13 is/are rejected under 35 U.S.C. 112(a). Representative claim(s) 1, 12, and 13 recite(s) “outputting, by the computer, information indicating the subject staff and the candidate place of assignment which has been inferred.”
Examiner notes the bolded portion of the representative claims above is new matter. Initially, Examiner notes the bolded portion recites “outputting, by the computer, information indicating the subject staff” which does not appear to be supported by the originally filed disclosure. Examiner notes the closest portions of the original disclosure include [0103, 0102] which only states
“In S208, the output section 210 outputs the candidate place of assignment inferred in S206. The output section 210 may be configured to output, together with the candidate place of assignment inferred in S206, the basis information generated in S207.” and
“Specifically, basis information may be generated which includes a degree of similarity between (i) an attribute of the subject staff and (ii) an attribute of a person who belongs to the candidate place of assignment inferred by the inference section 208 in S206.”
None of these portions however disclose the above bolded claim language. Appropriate correction/clarification is required. Claim(s) 2-10 and 12-19 is/are rejected because they depend on claim(s) 1, 11, and 20.
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.
Claim(s) 1-2 and 4-13 is/are rejected under 35 U.S.C. 101 because the claimed invention is directed to a judicial exception (i.e., a law of nature, a natural phenomenon, or an abstract idea) without significantly more.
The limitation(s) below for representative claim(s) 1, 12, and 13 that, under its broadest reasonable interpretation, is directed to recruitment assistance.
Step 1: The claim(s) as drafted, is/are a process (claim(s) 12 recites a series of steps) and system (claim(s) 1-2, 4-11, and 13 recites a series of components).
Step 2A – Prong 1: The claimed invention is directed to an abstract idea without significantly more. The claim(s) recite(s) (emphasis added):
Claim 12: receiving, by a computer, a request pertaining to a place of assignment of a subject staff;
performing, by the computer, a machine learning operation to generate an acceptance place graph that is a learned model and includes (i) a plurality of nodes each pertaining to (a) an acceptance place which is likely to accept the subject staff, (b) a skill of each of a plurality of persons, or (c) work experience of each of the plurality of persons, (ii) links indicating relationships between the plurality of nodes, and (iii) a relation between a skill or work experience of each of the plurality of persons and an affiliation of each of the plurality of persons;
inferring, by the computer, based on an output of the learned model, a candidate place of assignment that is of the subject staff and that conforms to the request; and
outputting, by the computer, information indicating the subject staff and the candidate place of assignment which has been inferred.
Claim 1 and 13: the same analysis as claim(s) 12.
Dependent claims 2 and 4-11 recite the same or similar abstract idea(s) as independent claim(s) 1, 12, and 13 with merely a further narrowing of the abstract idea(s) of: .
The identified limitations of the independent and dependent claims above fall well-within the groupings of subject matter identified by the courts as being abstract concepts of:
a method of organizing human activity (commercial or legal interactions including advertising, marketing or sales activities or behaviors, or business relations) because the invention is directed to economic and/or business relationships as they are associated with recruitment assistance.
Step 2A – Prong 2: This judicial exception is not integrated into a practical application because:
The additional elements encompassed by the abstract idea include machine learning operation (claim(s) 1, 12, and 13), apparatus comprising at least one processor (claim(s) 1), computer (claim(s) 12), non-transitory machine-readable medium, memory a computer-readable non-transitory storage medium (claim(s) 13), processor (claims 2, 4, 6-11).
The claim(s) does/do not include additional elements that are sufficient to amount to significantly more than the judicial exception because the additional elements as described above with respect to Step 2A Prong 2 fails to describe:
Improvements to the functioning of a computer, or to any other technology or technical field - see MPEP 2106.05(a)
Applying or using a judicial exception to effect a particular treatment or prophylaxis for a disease or medical condition – see Vanda Memo
Applying the judicial exception with, or by use of, a particular machine – see MPEP 2106.05(b)
Effecting a transformation or reduction of a particular article to a different state or thing - see MPEP 2106.05(c)
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 - see MPEP 2106.05(e) and Vanda Memo.
Thus the additional elements as described above with respect to Step 2A Prong 2 are merely (as additionally noted by instant specification [0251-0254]) invoked as a tool and/or general purpose computer to apply instructions of an abstract idea in a particular technological environment, and/or mere application of an abstract idea in a particular technological environment and merely limiting the use of an abstract idea to a particular technological field do not integrate an abstract idea into a practical application (MPEP 2106.05(f)&(h)).
Step 2B: The claims do not include additional elements that are sufficient to amount to significantly more than the judicial exception. Thus the additional elements as described above with respect to Step 2A Prong 2 are merely (as additionally noted by instant specification [0251-0254]) invoked as a tool and/or a general purpose computer to apply instructions of an abstract idea in a particular technological environment, and/or mere application of an abstract idea in a particular technological environment and merely limiting the use of an abstract idea to a particular technological field do not integrate an abstract idea into a practical application and thus similarly the combination and arrangement of the above identified additional elements when analyzed under Step 2B also fails to necessitate a conclusion that the claims amount to significantly more than the abstract idea for the same reasons as set forth above (MPEP 2106.05(f)&(h)).
Claim Rejections - 35 USC § 103
The following is a quotation of 35 U.S.C. 103 which forms the basis for all obviousness rejections set forth in this Office action:
A patent for a claimed invention may not be obtained, notwithstanding that the claimed invention is not identically disclosed as set forth in section 102, if the differences between the claimed invention and the prior art are such that the claimed invention as a whole would have been obvious before the effective filing date of the claimed invention to a person having ordinary skill in the art to which the claimed invention pertains. Patentability shall not be negated by the manner in which the invention was made.
The factual inquiries set forth in Graham v. John Deere Co., 383 U.S. 1, 148 USPQ 459 (1966), that are applied for establishing a background for determining obviousness under 35 U.S.C. 103 are summarized as follows:
1. Determining the scope and contents of the prior art.
2. Ascertaining the differences between the prior art and the claims at issue.
3. Resolving the level of ordinary skill in the pertinent art.
4. Considering objective evidence present in the application indicating obviousness or nonobviousness.
It has been held that a prior art reference must either be in the field of applicant’s endeavor or, if not, then be reasonably pertinent to the particular problem with which the applicant was concerned, in order to be relied upon as a basis for rejection of the claimed invention. See In re Oetiker, 977 F.2d 1443, 24 USPQ2d 1443 (Fed. Cir. 1992).
Claim(s) 1-2 and 12-13 is/are rejected under 35 U.S.C. 103 as being unpatentable over Tsukamoto (JP 2020-77361 A) in view of Polli et al. (US 2021/0264371 A1).
Regarding claim 1, 12, and 13 (currently amended), Tsukamoto teaches a recruitment assistance method comprising:
{a recruitment assistance apparatus comprising at least one processor, the at least one processor carrying out: - claim 1}
{a computer-readable non-transitory storage medium storing a recruitment assistance program for causing a computer to carry out - claim 13}
receiving, by a computer, a request pertaining to a place of assignment of a subject staff [see at least [0043] “As shown in the lower section of FIG. 1 , the prediction device 1 is an information processing device such as a smartphone, a tablet computer, a general-purpose computer, or a workstation, and is configured with a central processing unit (CPU), a main memory (RAM), a read-only memory (ROM), an input/output device (I/O), and, if necessary, an external storage device such as a hard disk device (not shown), although these are not shown in the figure because they are built into the device.
As described later, the prediction device 1 also has the functions of a learning model construction device (hereinafter referred to as the "construction device").
Furthermore, the prediction device 1 does not necessarily have to be configured as a single device, but may be configured as a plurality of devices communicably connected to each other via a network.”;
[0069-0073] describes an after-joining evaluation prediction device, wherein examination data and individual history data of a person who wishes to join a company are acquired, department-by-department evaluation on the person after joining the company is predicted using a learning model that has learned the relationship between the respective examination data and individual history data of multiple existing employees and the respective evaluation data of the existing employees, and the prediction result is displayed “Once the necessary data and information have been collected as described above, the evaluation prediction means 55 predicts the post-employment evaluation of each job applicant based on the applicant test data and the optimal prediction model (learning model) (step 25).
The prediction result is displayed on the display means 9 (step 27). This ends the program. By displaying it on the display means, users (forecasters) who see it can know the predicted evaluation of each applicant after joining the company, and can thereby obtain a useful clue for selecting employees.”];
performing, by the computer, generate an acceptance place profile that includes (i) a plurality of nodes each pertaining to (a) an acceptance place which is likely to accept the subject staff, (b) a skill of each of a plurality of persons, or (c) work experience of each of the plurality of persons, and performing, by the computer, a machine learning operation to generate (iii) a relation between a skill or work experience of each of the plurality of persons and an affiliation of each of the plurality of persons;
inferring, by the computer, based on an output of the learned model, a candidate place of assignment that is of the subject staff and that conforms to the request [see at least [0018, 0072-0073] “According to the prediction device of claim 6, it is possible to predict with high accuracy the degree of activity of a job applicant after joining the company, including the possibility of him or her quitting.
In particular, when using a learning model constructed by the device of claim 2, the personal history data including the history and work history of each existing employee is also taken into account, so that it reflects, for example, the industry, business type, and size of the company, as well as differences in departments, superiors, and colleague environments within the company, making it possible to provide a learning model that is more suited to the actual situation of the company.”;
[0039-0041] “In this specification, "personal career data including history and work history" of existing employees refers to the history and work history of existing employees, as well as data and information regarding, for example, job type, rank (for example, ordinary employee, manager, executive level), assigned department (for example, can be identified by section, division, or business division), assigned superior (for example, can be identified by section manager A, department manager B, business division manager C, or a combination of these), evaluation scores and evaluators from recruitment interviews up to the final interview (such as interview scores).
…
In this specification, "supervised learning" refers to a type of machine learning in which data given in advance is regarded as advice from a teacher and used as a guide for learning.
Good examples of supervised learning include decision trees, random forests, neural networks, deep learning, logistic regression, support vector machines, naive Bayes, boosting, bagging, and proprietary algorithms built by combining these. Of course, it goes without saying that supervised learning methods other than those mentioned above can also be freely adopted.
In this specification, the term "employee applicants" is used in a broad sense.
This includes not only those who literally wish to join a company, but also existing employees who wish to transfer to another department or have not yet been ordered to do so. In other words, other departments in the latter intra-company transfers correspond to the former company. In other words, the purpose is to predict how well an individual will be able to perform in a new environment (a company or other department) if they were to become part of that new environment.”;
[0069-0071] using machine learning to infer candidate placement “Once the necessary data and information have been collected as described above, the evaluation prediction means 55 predicts the post-employment evaluation of each job applicant based on the applicant test data and the optimal prediction model (learning model) (step 25).”;
[0052-0053, 0055-0057] training of machine learning model;
[0054, 0058] machine learning model components]; and
outputting, by the computer, information indicating the subject staff and the candidate place of assignment which has been inferred [as noted by the 112 rejection above the limitation does not have support and is interpreted as outputting, by the computer, information indicating the candidate place of assignment which has been inferred,
then see at least [0069-0073] describes an after-joining evaluation prediction device, wherein examination data and individual history data of a person who wishes to join a company are acquired, department-by-department evaluation on the person after joining the company is predicted using a learning model that has learned the relationship between the respective examination data and individual history data of multiple existing employees and the respective evaluation data of the existing employees, and the prediction result is displayed “Once the necessary data and information have been collected as described above, the evaluation prediction means 55 predicts the post-employment evaluation of each job applicant based on the applicant test data and the optimal prediction model (learning model) (step 25).
The prediction result is displayed on the display means 9 (step 27). This ends the program. By displaying it on the display means, users (forecasters) who see it can know the predicted evaluation of each applicant after joining the company, and can thereby obtain a useful clue for selecting employees.”].
Tsukamoto teaches candidate placement recommendation but doesn’t/don’t explicitly teach however, in the field pertinent to the particular problem with which the applicant was concerned such as talent identification, Polli discloses
performing, by the computer, a machine learning operation to generate an acceptance place graph that is a learned model and includes (i) a plurality of nodes each pertaining to (a) an acceptance place which is likely to accept the subject staff, (b) a skill of each of a plurality of persons, or (c) work experience of each of the plurality of persons, (ii) links indicating relationships between the plurality of nodes, [see at least [0336] “The IoT system may organize the roles of an organization into a role directed graph that may be a tree structure. Each node of the role directed graph may be associated with a role and a model of success indicating suitability for that role that may be based on a set or a subset of games. The link connecting a parent node to a child node may be associated with a criterion for selecting that child node based on the performance on a set or a subset of games (e.g., measurements derived from the set of games of ancestor nodes). A leaf node may be associated with no role indicating that there is no role along the path from the root node to that leaf node. In this case, the system may suggest roles outside the organization based on the candidate's performance on the games played during the assessment for the organization. Rather than indicating no role, a leaf node may indicate one or more other possible organizations or one or more divisions of the organization. Each possible organization or division may be associated with its own role tree structure.”;
[0337] “The IoT system may allow a tree structure to be defined for each organization (e.g., company), each division (e.g., engineering) within an organization, each subsidiary of an organization, and so on.”;
[0338] “FIG. 41 illustrates an example role tree structure for an organization. The role tree structure includes nodes 4101N-4116N and links 4102L-4116L. The role tree structure is associated with an instrument division of an organization. The description within each node represents the role of that node. For example, node 4101N indicates the role of instrument division president, node 4111N indicates the role of human resources manager, node 4112N indicates no role, and node 4114N indicates to next apply the role tree structure of the biotechnology division of the organization. Each node may be associated with a set or a subset of games. Each link may be associated with a criterion for selecting that link, that is determining whether an assessment is to be made of suitability for the role of the node to which the link points—assuming that the candidate is not suitable for the role of the parent node. For example, if a candidate is not suitable to be president of the instrument division, then that candidate may satisfy the criterion associated with links 4102L and 4105L but may not satisfy the criterion associated with links 4103L and 4104L. The use of such criterion allows the playing of the games associated with a child node to be avoided when it is clear that the candidate is not suitable for the role of the child node based on performance on the games associated with the ancestor nodes. In some embodiments, the role directed graph may not be a tree structure because a node may have multiple parent nodes. For example, link 4117L and link 4117L′ indicate node 4110N has two parent node 4102N and node 4104N. If a candidate is determined not to be suitable to be a chief engineer or a marketing manager, then the suitability of the candidate to be an engineering manager may be assessed.”;
[0118] “Accordingly, the employee model may be based upon a target group of top employees out of all employees who are taking the neuroscience-based tests (playing the games)”].
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify Tsukamoto with Polli to include the limitation(s) above as disclosed by Polli. Doing so would help improve Tsukamoto’s (Tsukamoto [0004-0006] ) process “to enable recruitment activities to be carried out while predicting the post-employment evaluation of the personnel to be recruited from various angles, and also to eliminate mismatches that may lead to employee turnover as much as possible” via clarifying what metrics are used [see at least Polli [0002-0004] ].
Furthermore, all of the claimed elements were known in the prior arts of a) Tsukamoto and b) Polli and c) one skilled in the art could have combined the elements as claimed by known methods with no change in their respective functions, and the combination would have yielded predictable results to one of ordinary skill in the art before the effective filing date of the claimed invention.
Regarding claim 2, modified Tsukamoto teaches the recruitment assistance apparatus according to claim 1, .
Modified Tsukamoto (Tsukamoto) teaches candidate placement recommendation but doesn’t/don’t explicitly teach however, in the field pertinent to the particular problem with which the applicant was concerned such as talent identification, Polli discloses
wherein: the at least one processor further carries out a basis information generation process of generating, as a basis of inference, basis information that includes a degree of similarity between (i) an attribute of the subject staff and (ii) an attribute of a person who belongs to the candidate place of assignment which has been inferred in the inference process; and
in the output process, the at least one processor further outputs the basis information [for the limitations above, see at least [0209-0210] “The models for supporting screening and sourcing may be identical. For sourcing, a user (e.g., a recruiter) may use the sourcing models to identify candidates who are most similar to a target group of individuals (i.e., identify candidates who match closely to an employee model), and present those candidates to a company for its hiring needs. For screening, a company may use the screening models to screen a pool of candidates that the company has already selected. In sourcing, a recruiter may present only candidates who pass a threshold cut-off to a company, and need not share data on candidates who do not pass the threshold cut-off. In contrast, a company may receive data on all of the candidates in screening, regardless whether each candidate passes the threshold cut-off.
As previously described, an employee model may be representative of a target group of top employees of the company, and the employee model may be contrasted against a baseline group. The baseline group may be selected from a database comprising of employees from other fields, who may be similar to the target group of employees taking the neuroscience-based tests in terms of demographic factors such as gender, age, ethnicity, educational background, but who do not work in in the same field as the target group of employees. Candidates may be compared to the employee model to determine their match/compatibility for a specific job position with the company.”;
[0118] “Accordingly, the employee model may be based upon a target group of top employees out of all employees who are taking the neuroscience-based tests (playing the games)”].
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify modified Tsukamoto with Polli to include the limitation(s) above as disclosed by Polli. Doing so would help improve modified Tsukamoto’s (Tsukamoto [0004-0006] ) process “to enable recruitment activities to be carried out while predicting the post-employment evaluation of the personnel to be recruited from various angles, and also to eliminate mismatches that may lead to employee turnover as much as possible” via clarifying what metrics are used [see at least Polli [0002-0004] ].
Furthermore, all of the claimed elements were known in the prior arts of a) modified Tsukamoto and b) Polli and c) one skilled in the art could have combined the elements as claimed by known methods with no change in their respective functions, and the combination would have yielded predictable results to one of ordinary skill in the art before the effective filing date of the claimed invention.
Claim(s) 4-11 is/are rejected under 35 U.S.C. 103 as being unpatentable over Tsukamoto in view of Polli as applied to claim(s) 1 above and further in view of Luo et al. (US 2021/0034975 A1).
Regarding claim 4 (currently amended), modified Tsukamoto teaches the recruitment assistance apparatus according to claim 1, as well as acceptance place graph
and Tsukamoto teaches using a subject staff profile including a plurality of data pertaining to the subject staff and the acceptance place profile, a staff profile which links to a data included in the subject staff profile from among staff profiles which are included in the acceptance place profile and which indicate respective staffs belonging to the acceptance place, subject staff profile and the acceptance place profile; and
in the inference process, the at least one processor infers the candidate place of assignment that is of the subject staff and that conforms to the request on the basis of the staff profile [see at least [0018, 0072-0073] “According to the prediction device of claim 6, it is possible to predict with high accuracy the degree of activity of a job applicant after joining the company, including the possibility of him or her quitting.
In particular, when using a learning model constructed by the device of claim 2, the personal history data including the history and work history of each existing employee is also taken into account, so that it reflects, for example, the industry, business type, and size of the company, as well as differences in departments, superiors, and colleague environments within the company, making it possible to provide a learning model that is more suited to the actual situation of the company.”;
[0039-0041] “In this specification, "personal career data including history and work history" of existing employees refers to the history and work history of existing employees, as well as data and information regarding, for example, job type, rank (for example, ordinary employee, manager, executive level), assigned department (for example, can be identified by section, division, or business division), assigned superior (for example, can be identified by section manager A, department manager B, business division manager C, or a combination of these), evaluation scores and evaluators from recruitment interviews up to the final interview (such as interview scores).
…
In this specification, "supervised learning" refers to a type of machine learning in which data given in advance is regarded as advice from a teacher and used as a guide for learning.
Good examples of supervised learning include decision trees, random forests, neural networks, deep learning, logistic regression, support vector machines, naive Bayes, boosting, bagging, and proprietary algorithms built by combining these. Of course, it goes without saying that supervised learning methods other than those mentioned above can also be freely adopted.
In this specification, the term "employee applicants" is used in a broad sense.
This includes not only those who literally wish to join a company, but also existing employees who wish to transfer to another department or have not yet been ordered to do so. In other words, other departments in the latter intra-company transfers correspond to the former company. In other words, the purpose is to predict how well an individual will be able to perform in a new environment (a company or other department) if they were to become part of that new environment.”;
[0069-0071] using machine learning to infer candidate placement “Once the necessary data and information have been collected as described above, the evaluation prediction means 55 predicts the post-employment evaluation of each job applicant based on the applicant test data and the optimal prediction model (learning model) (step 25).”;
[0069-0073] describes an after-joining evaluation prediction device, wherein examination data and individual history data of a person who wishes to join a company are acquired, department-by-department evaluation on the person after joining the company is predicted using a learning model that has learned the relationship between the respective examination data and individual history data of multiple existing employees and the respective evaluation data of the existing employees, and the prediction result is displayed “Once the necessary data and information have been collected as described above, the evaluation prediction means 55 predicts the post-employment evaluation of each job applicant based on the applicant test data and the optimal prediction model (learning model) (step 25).
The prediction result is displayed on the display means 9 (step 27). This ends the program. By displaying it on the display means, users (forecasters) who see it can know the predicted evaluation of each applicant after joining the company, and can thereby obtain a useful clue for selecting employees.”].
Modified Tsukamoto teaches candidate placement recommendation but doesn’t/don’t explicitly teach but Polli discloses
the acceptance place graph, a staff node which links to a node included in the subject staff graph from among staff nodes which are included in the acceptance place graph and which indicate respective staffs belonging to the acceptance place, the acceptance place graph; and
in the inference process, the at least one processor infers a candidate place of assignment that is of the subject staff and that conforms to the request on the basis of the staff which has been predicted [for the limitations above, see at least [0336] “The IoT system may organize the roles of an organization into a role directed graph that may be a tree structure. Each node of the role directed graph may be associated with a role and a model of success indicating suitability for that role that may be based on a set or a subset of games. The link connecting a parent node to a child node may be associated with a criterion for selecting that child node based on the performance on a set or a subset of games (e.g., measurements derived from the set of games of ancestor nodes). A leaf node may be associated with no role indicating that there is no role along the path from the root node to that leaf node. In this case, the system may suggest roles outside the organization based on the candidate's performance on the games played during the assessment for the organization. Rather than indicating no role, a leaf node may indicate one or more other possible organizations or one or more divisions of the organization. Each possible organization or division may be associated with its own role tree structure.”;
[0337] “The IoT system may allow a tree structure to be defined for each organization (e.g., company), each division (e.g., engineering) within an organization, each subsidiary of an organization, and so on.”;
[0338] “FIG. 41 illustrates an example role tree structure for an organization. The role tree structure includes nodes 4101N-4116N and links 4102L-4116L. The role tree structure is associated with an instrument division of an organization. The description within each node represents the role of that node. For example, node 4101N indicates the role of instrument division president, node 4111N indicates the role of human resources manager, node 4112N indicates no role, and node 4114N indicates to next apply the role tree structure of the biotechnology division of the organization. Each node may be associated with a set or a subset of games. Each link may be associated with a criterion for selecting that link, that is determining whether an assessment is to be made of suitability for the role of the node to which the link points—assuming that the candidate is not suitable for the role of the parent node. For example, if a candidate is not suitable to be president of the instrument division, then that candidate may satisfy the criterion associated with links 4102L and 4105L but may not satisfy the criterion associated with links 4103L and 4104L. The use of such criterion allows the playing of the games associated with a child node to be avoided when it is clear that the candidate is not suitable for the role of the child node based on performance on the games associated with the ancestor nodes. In some embodiments, the role directed graph may not be a tree structure because a node may have multiple parent nodes. For example, link 4117L and link 4117L′ indicate node 4110N has two parent node 4102N and node 4104N. If a candidate is determined not to be suitable to be a chief engineer or a marketing manager, then the suitability of the candidate to be an engineering manager may be assessed.”].
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify modified Tsukamoto with Polli to include the limitation(s) above as disclosed by Polli. Doing so would help improve modified Tsukamoto’s (Tsukamoto [0004-0006] ) process “to enable recruitment activities to be carried out while predicting the post-employment evaluation of the personnel to be recruited from various angles, and also to eliminate mismatches that may lead to employee turnover as much as possible” via clarifying what metrics are used [see at least Polli [0002-0004] ].
Furthermore, all of the claimed elements were known in the prior arts of a) modified Tsukamoto and b) Polli and c) one skilled in the art could have combined the elements as claimed by known methods with no change in their respective functions, and the combination would have yielded predictable results to one of ordinary skill in the art before the effective filing date of the claimed invention.
Modified Tsukamoto teaches candidate placement recommendation but doesn’t/don’t explicitly teach however, in the field pertinent to the particular problem with which the applicant was concerned such as similarities between users via graph analysis, Luo discloses
wherein: the at least one processor further carries out a link prediction process of predicting, by link prediction using a subject staff graph including a plurality of nodes pertaining to the subject staff and the other graph, a node which links to a node included in the subject staff graph from among nodes which are included in the other graph, the link prediction being carried out for predicting a relationship between nodes which are not connected to each other by a link in the subject staff graph and the other graph; and
a candidate place of assignment that is of the subject staff and that conforms to the request on the basis of the staff node which has been predicted in the link prediction process [for the limitations above, see at least [0033] “FIG. 3 depicts an exemplary representation of a job position-to-job position directed graph 300. The job position data in the directed graphs includes both actual job position data and synthetic job position data derived from the actual job position data.”;
[0035] “Referring to FIG. 3, the directed graph 300 depicts an exemplary pair-wise representation of an individual job seeker's three job positions s1, s2, and s3. The job positions s1, s2, and s3 are (job title, company) pairs extracted from the employment history of the job seeker's resumé and the directional ordering is based associated employment dates.”;
[0036] where the various job seekers include various subject staff and acceptance place staff “The directed graph 400 consolidates each job seeker's individual directed graph g into a collective job position-to-job position transition directed graph G such as directed graph 400. “G” represents the collective vertices V and edges E of any job position-to-job position directed graph, where g ∈ G. In at least one embodiment, the directed graph G consolidates like vertices and edges from each directed graph g of each job seeker. (Directed graph 300 represents one exemplary embodiment of a directed graph g from a job seeker.) Accordingly, G=(V, E) represents the consolidated directed graphs from sets of job seeker historical job transition data present in actual job data 102 and synthetic data derived therefrom as, for example, subsequently discussed, where V represents the set of all vertices in G, and E represents the set of all edges in G. … For example, if job seeker A has job positions and job position-to-job position transitions of s1→to→s2→s3 and job seeker B has job positions and job position-to-job position transitions of s2→s3→s4, then AI job recommender system 100 creates four unique vertices, one each for job positions s1, s2, s3, and s4 -and weighted edges that reflect two transitions from vertice s2 and one transition from vertices s1 and s3.”;
[0038] “In at least one embodiment, operation 504 represents a quantity of common edges among the job seeker's directed graphs by weighting edges with weights wij representing the frequency of each particular job position-to-job position transitions, i.e. each vertice to vertice transition in the directed graph 400). … In at least one embodiment, in operation 506, AI job recommender system 100 normalizes each weight wij to obtain a job position-to-job position transition (vertice to vertice) transition probability … The process of determining whether a job position (vertice) in one resumé is equivalent to a job position in another resumé is a matter of design choice. In at least one embodiment, AI job recommender system 100 determines two job positions to be similar when job seekers are very likely to move from one job position to another and vice versa (first-order proximity). … Classifying the job position-to-job position transition data for the first job seeker with the optimized, job-to-job transition vector space to predict one or more job transitions for the job seeker in accordance with each transition probability pij so that higher transition probability pij indicate a higher likelihood and higher preference job transition.”].
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify modified Tsukamoto with Luo to include the limitation(s) above as disclosed by Luo. Doing so would help improve modified Tsukamoto’s (Tsukamoto [0004-0006] ) process “to enable recruitment activities to be carried out while predicting the post-employment evaluation of the personnel to be recruited from various angles, and also to eliminate mismatches that may lead to employee turnover as much as possible” via clarifying what metrics are used [see at least Luo [0002-0005] ].
Furthermore, all of the claimed elements were known in the prior arts of a) modified Tsukamoto and b) Luo and c) one skilled in the art could have combined the elements as claimed by known methods with no change in their respective functions, and the combination would have yielded predictable results to one of ordinary skill in the art before the effective filing date of the claimed invention.
Regarding claim 5, modified Tsukamoto teaches the recruitment assistance apparatus according to claim 4, .
Modified Tsukamoto doesn’t/don’t explicitly teach but Luo discloses
wherein: the subject staff graph includes (i) a plurality of nodes each pertaining to a skill or work experience of the subject staff and (ii) links each indicating a relationship between nodes [see at least [0033] “FIG. 3 depicts an exemplary representation of a job position-to-job position directed graph 300. The job position data in the directed graphs includes both actual job position data and synthetic job position data derived from the actual job position data.”;
[0035] “Referring to FIG. 3, the directed graph 300 depicts an exemplary pair-wise representation of an individual job seeker's three job positions s1, s2, and s3. The job positions s1, s2, and s3 are (job title, company) pairs extracted from the employment history of the job seeker's resumé and the directional ordering is based associated employment dates.”].
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify modified Tsukamoto with Luo to include the limitation(s) above as disclosed by Luo. Doing so would help improve modified Tsukamoto’s (Tsukamoto [0004-0006] ) process “to enable recruitment activities to be carried out while predicting the post-employment evaluation of the personnel to be recruited from various angles, and also to eliminate mismatches that may lead to employee turnover as much as possible” via clarifying what metrics are used [see at least Luo [0002-0004] ].
Furthermore, all of the claimed elements were known in the prior arts of a) modified Tsukamoto and b) Luo and c) one skilled in the art could have combined the elements as claimed by known methods with no change in their respective functions, and the combination would have yielded predictable results to one of ordinary skill in the art before the effective filing date of the claimed invention.
Regarding claim 6, modified Tsukamoto teaches the recruitment assistance apparatus according to claim 4, as well as
the subject staff from among the staffs who belong to the acceptance place,
the staffs belonging to the acceptance place, and
the acceptance place graph.
Modified Tsukamoto teaches candidate placement recommendation but doesn’t/don’t explicitly teach but Polli discloses
the at least one processor further carries out an identification process of identifying, from among the staffs, a congenial staff who is indicated to be congenial to the staff by data [for the limitations above, see at least [0272] “Performance on the Trust Task can be associated with personality measures including Machiavellianism, and relational motives, for example, high concern for others and low concern for self. Participation in trust tasks can influence neurophysiological responses, for example, the production of oxytocin, and can be associated with the location, magnitude, and timing of neural responses in areas of the brain related to trust and social relationships.”;
[0273] random participant such as two people in same department “In a system of the invention, subjects were paired with a random participant.”;
[0209-0210] “The models for supporting screening and sourcing may be identical. For sourcing, a user (e.g., a recruiter) may use the sourcing models to identify candidates who are most similar to a target group of individuals (i.e., identify candidates who match closely to an employee model), and present those candidates to a company for its hiring needs. For screening, a company may use the screening models to screen a pool of candidates that the company has already selected. In sourcing, a recruiter may present only candidates who pass a threshold cut-off to a company, and need not share data on candidates who do not pass the threshold cut-off. In contrast, a company may receive data on all of the candidates in screening, regardless whether each candidate passes the threshold cut-off.
As previously described, an employee model may be representative of a target group of top employees of the company, and the employee model may be contrasted against a baseline group. The baseline group may be selected from a database comprising of employees from other fields, who may be similar to the target group of employees taking the neuroscience-based tests in terms of demographic factors such as gender, age, ethnicity, educational background, but who do not work in in the same field as the target group of employees. Candidates may be compared to the employee model to determine their match/compatibility for a specific job position with the company.”;
[0118] “Accordingly, the employee model may be based upon a target group of top employees out of all employees who are taking the neuroscience-based tests (playing the games)”].
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify modified Tsukamoto with Polli to include the limitation(s) above as disclosed by Polli. Doing so would help improve modified Tsukamoto’s (Tsukamoto [0004-0006] ) process “to enable recruitment activities to be carried out while predicting the post-employment evaluation of the personnel to be recruited from various angles, and also to eliminate mismatches that may lead to employee turnover as much as possible” via clarifying what metrics are used [see at least Polli [0002-0004] ].
Furthermore, all of the claimed elements were known in the prior arts of a) modified Tsukamoto and b) Polli and c) one skilled in the art could have combined the elements as claimed by known methods with no change in their respective functions, and the combination would have yielded predictable results to one of ordinary skill in the art before the effective filing date of the claimed invention.
Modified Tsukamoto doesn’t/don’t explicitly teach but Luo discloses
wherein: in the link prediction process, the at least one processor predicts, by the link prediction, a similar staff who is similar to the subject staff; and
the at least one processor further carries out an identification process of identifying, a staff who is indicated to be related to the similar staff by nodes and links included in the graph [for the limitations above, see at least [0033] “FIG. 3 depicts an exemplary representation of a job position-to-job position directed graph 300. The job position data in the directed graphs includes both actual job position data and synthetic job position data derived from the actual job position data.”;
[0035] “Referring to FIG. 3, the directed graph 300 depicts an exemplary pair-wise representation of an individual job seeker's three job positions s1, s2, and s3. The job positions s1, s2, and s3 are (job title, company) pairs extracted from the employment history of the job seeker's resumé and the directional ordering is based associated employment dates.;
[0036] where the various job seekers include various subject staff and acceptance place staff “The directed graph 400 consolidates each job seeker's individual directed graph g into a collective job position-to-job position transition directed graph G such as directed graph 400. “G” represents the collective vertices V and edges E of any job position-to-job position directed graph, where g ∈ G. In at least one embodiment, the directed graph G consolidates like vertices and edges from each directed graph g of each job seeker. (Directed graph 300 represents one exemplary embodiment of a directed graph g from a job seeker.) Accordingly, G=(V, E) represents the consolidated directed graphs from sets of job seeker historical job transition data present in actual job data 102 and synthetic data derived therefrom as, for example, subsequently discussed, where V represents the set of all vertices in G, and E represents the set of all edges in G. … For example, if job seeker A has job positions and job position-to-job position transitions of s1→to→s2→s3 and job seeker B has job positions and job position-to-job position transitions of s2→s3→s4, then AI job recommender system 100 creates four unique vertices, one each for job positions s1, s2, s3, and s4 -and weighted edges that reflect two transitions from vertice s2 and one transition from vertices s1 and s3.”;
[0038] match users based similar job paths thus determine similar users “In at least one embodiment, operation 504 represents a quantity of common edges among the job seeker's directed graphs by weighting edges with weights wij representing the frequency of each particular job position-to-job position transitions, i.e. each vertice to vertice transition in the directed graph 400). … In at least one embodiment, in operation 506, AI job recommender system 100 normalizes each weight wij to obtain a job position-to-job position transition (vertice to vertice) transition probability … The process of determining whether a job position (vertice) in one resumé is equivalent to a job position in another resumé is a matter of design choice. In at least one embodiment, AI job recommender system 100 determines two job positions to be similar when job seekers are very likely to move from one job position to another and vice versa (first-order proximity). … Classifying the job position-to-job position transition data for the first job seeker with the optimized, job-to-job transition vector space to predict one or more job transitions for the job seeker in accordance with each transition probability pij so that higher transition probability pij indicate a higher likelihood and higher preference job transition.”].
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify modified Tsukamoto with Luo to include the limitation(s) above as disclosed by Luo. Doing so would help improve modified Tsukamoto’s (Tsukamoto [0004-0006] ) process “to enable recruitment activities to be carried out while predicting the post-employment evaluation of the personnel to be recruited from various angles, and also to eliminate mismatches that may lead to employee turnover as much as possible” via clarifying what metrics are used [see at least Luo [0002-0004] ].
Furthermore, all of the claimed elements were known in the prior arts of a) modified Tsukamoto and b) Luo and c) one skilled in the art could have combined the elements as claimed by known methods with no change in their respective functions, and the combination would have yielded predictable results to one of ordinary skill in the art before the effective filing date of the claimed invention.
Regarding claim 7, modified Tsukamoto teaches the recruitment assistance apparatus according to claim 6, as well as
the acceptance place and
each of the staffs belonging to the acceptance place.
Modified Tsukamoto doesn’t/don’t explicitly teach but Luo discloses
wherein: the at least one processor further carries out a congeniality determination process of determining congeniality between the subject staff and the other users based on a degree of similarity that indicates a degree to which the congenial staff is similar to each of the staffs [for the limitations above, see at least [0033] “FIG. 3 depicts an exemplary representation of a job position-to-job position directed graph 300. The job position data in the directed graphs includes both actual job position data and synthetic job position data derived from the actual job position data.”;
[0035] “Referring to FIG. 3, the directed graph 300 depicts an exemplary pair-wise representation of an individual job seeker's three job positions s1, s2, and s3. The job positions s1, s2, and s3 are (job title, company) pairs extracted from the employment history of the job seeker's resumé and the directional ordering is based associated employment dates.;
[0036] where the various job seekers include various subject staff and acceptance place staff “The directed graph 400 consolidates each job seeker's individual directed graph g into a collective job position-to-job position transition directed graph G such as directed graph 400. “G” represents the collective vertices V and edges E of any job position-to-job position directed graph, where g ∈ G. In at least one embodiment, the directed graph G consolidates like vertices and edges from each directed graph g of each job seeker. (Directed graph 300 represents one exemplary embodiment of a directed graph g from a job seeker.) Accordingly, G=(V, E) represents the consolidated directed graphs from sets of job seeker historical job transition data present in actual job data 102 and synthetic data derived therefrom as, for example, subsequently discussed, where V represents the set of all vertices in G, and E represents the set of all edges in G. … For example, if job seeker A has job positions and job position-to-job position transitions of s1→to→s2→s3 and job seeker B has job positions and job position-to-job position transitions of s2→s3→s4, then AI job recommender system 100 creates four unique vertices, one each for job positions s1, s2, s3, and s4 -and weighted edges that reflect two transitions from vertice s2 and one transition from vertices s1 and s3.”;
[0038] match users based similar job paths thus determine similar users “In at least one embodiment, operation 504 represents a quantity of common edges among the job seeker's directed graphs by weighting edges with weights wij representing the frequency of each particular job position-to-job position transitions, i.e. each vertice to vertice transition in the directed graph 400). … In at least one embodiment, in operation 506, AI job recommender system 100 normalizes each weight wij to obtain a job position-to-job position transition (vertice to vertice) transition probability … The process of determining whether a job position (vertice) in one resumé is equivalent to a job position in another resumé is a matter of design choice. In at least one embodiment, AI job recommender system 100 determines two job positions to be similar when job seekers are very likely to move from one job position to another and vice versa (first-order proximity). … Classifying the job position-to-job position transition data for the first job seeker with the optimized, job-to-job transition vector space to predict one or more job transitions for the job seeker in accordance with each transition probability pij so that higher transition probability pij indicate a higher likelihood and higher preference job transition.”].
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify modified Tsukamoto with Luo to include the limitation(s) above as disclosed by Luo. Doing so would help improve modified Tsukamoto’s (Tsukamoto [0004-0006] ) process “to enable recruitment activities to be carried out while predicting the post-employment evaluation of the personnel to be recruited from various angles, and also to eliminate mismatches that may lead to employee turnover as much as possible” via clarifying what metrics are used [see at least Luo [0002-0004] ].
Furthermore, all of the claimed elements were known in the prior arts of a) modified Tsukamoto and b) Luo and c) one skilled in the art could have combined the elements as claimed by known methods with no change in their respective functions, and the combination would have yielded predictable results to one of ordinary skill in the art before the effective filing date of the claimed invention.
Regarding claim 8, modified Tsukamoto teaches the recruitment assistance apparatus according to claim 6, as well as
the congenial staff, and
the acceptance place
and Tsukamoto teaches a department to which the staff belongs from among a plurality of departments included in the place, and the department and the staff the staff each of staffs belonging to the department [see at least [0039] “In this specification, "personal career data including history and work history" of existing employees refers to the history and work history of existing employees, as well as data and information regarding, for example, job type, rank (for example, ordinary employee, manager, executive level), assigned department (for example, can be identified by section, division, or business division), assigned superior (for example, can be identified by section manager A, department manager B, business division manager C, or a combination of these), evaluation scores and evaluators from recruitment interviews up to the final interview (such as interview scores).”].
Modified Tsukamoto doesn’t/don’t explicitly teach but Polli discloses
wherein: the at least one processor further carries out a congeniality determination process of identifying a department to which the staff belongs, and determining the department [see at least [0128-0129] “A system of the invention can also be used by a subject to determine the subject's career propensities. Subjects who can use the invention include, for example, students, post-graduates, job seekers, and individuals seeking assistance regarding career planning. A subject can complete the tasks of the system, after which the system can create a profile for the subject based upon identified traits of the subject. A user can access a system of the invention from a computer system. The user can then complete the computerized tasks of the system using, for example, a computer, a laptop, a mobile device, or a tablet.
A subject's profile can be compared to a database of test subjects to score the subject and generate a model for the subject based on reference models. The test subjects can, for example, work for a business entity. The system can additionally generate a fit score for the subject based on the test subjects who work for a business entity and the test subjects' specific positions at the business entity. A system of the invention can recommend various industries to a subject based upon the subject's determined career propensity. Non-limiting examples of the industries that can be recommended by the system include consulting, education, healthcare, marketing, retail, entertainment, consumer products, entrepreneurship, technology, hedge funds, investment management, investment banking, private equity, product development, and product management.”].
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify modified Tsukamoto with Polli to include the limitation(s) above as disclosed by Polli. Doing so would help improve modified Tsukamoto’s (Tsukamoto [0004-0006] ) process “to enable recruitment activities to be carried out while predicting the post-employment evaluation of the personnel to be recruited from various angles, and also to eliminate mismatches that may lead to employee turnover as much as possible” via clarifying what metrics are used [see at least Polli [0002-0004] ].
Furthermore, all of the claimed elements were known in the prior arts of a) modified Tsukamoto and b) Polli and c) one skilled in the art could have combined the elements as claimed by known methods with no change in their respective functions, and the combination would have yielded predictable results to one of ordinary skill in the art before the effective filing date of the claimed invention.
Modified Tsukamoto doesn’t/don’t explicitly teach but Luo discloses
determining congeniality between the various users and the subject staff based on a degree of similarity that indicates a degree to which the congenial staff is similar to each of staffs belonging to the various users [see at least [0033] “FIG. 3 depicts an exemplary representation of a job position-to-job position directed graph 300. The job position data in the directed graphs includes both actual job position data and synthetic job position data derived from the actual job position data.”;
[0035] “Referring to FIG. 3, the directed graph 300 depicts an exemplary pair-wise representation of an individual job seeker's three job positions s1, s2, and s3. The job positions s1, s2, and s3 are (job title, company) pairs extracted from the employment history of the job seeker's resumé and the directional ordering is based associated employment dates.;
[0036] where the various job seekers include congenial staff and department staff “The directed graph 400 consolidates each job seeker's individual directed graph g into a collective job position-to-job position transition directed graph G such as directed graph 400. “G” represents the collective vertices V and edges E of any job position-to-job position directed graph, where g ∈ G. In at least one embodiment, the directed graph G consolidates like vertices and edges from each directed graph g of each job seeker. (Directed graph 300 represents one exemplary embodiment of a directed graph g from a job seeker.) Accordingly, G=(V, E) represents the consolidated directed graphs from sets of job seeker historical job transition data present in actual job data 102 and synthetic data derived therefrom as, for example, subsequently discussed, where V represents the set of all vertices in G, and E represents the set of all edges in G. … For example, if job seeker A has job positions and job position-to-job position transitions of s1→to→s2→s3 and job seeker B has job positions and job position-to-job position transitions of s2→s3→s4, then AI job recommender system 100 creates four unique vertices, one each for job positions s1, s2, s3, and s4 -and weighted edges that reflect two transitions from vertice s2 and one transition from vertices s1 and s3.”;
[0038] match users based similar job paths thus determine similar users “In at least one embodiment, operation 504 represents a quantity of common edges among the job seeker's directed graphs by weighting edges with weights wij representing the frequency of each particular job position-to-job position transitions, i.e. each vertice to vertice transition in the directed graph 400). … In at least one embodiment, in operation 506, AI job recommender system 100 normalizes each weight wij to obtain a job position-to-job position transition (vertice to vertice) transition probability … The process of determining whether a job position (vertice) in one resumé is equivalent to a job position in another resumé is a matter of design choice. In at least one embodiment, AI job recommender system 100 determines two job positions to be similar when job seekers are very likely to move from one job position to another and vice versa (first-order proximity). … Classifying the job position-to-job position transition data for the first job seeker with the optimized, job-to-job transition vector space to predict one or more job transitions for the job seeker in accordance with each transition probability pij so that higher transition probability pij indicate a higher likelihood and higher preference job transition.”].
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify modified Tsukamoto with Luo to include the limitation(s) above as disclosed by Luo. Doing so would help improve modified Tsukamoto’s (Tsukamoto [0004-0006] ) process “to enable recruitment activities to be carried out while predicting the post-employment evaluation of the personnel to be recruited from various angles, and also to eliminate mismatches that may lead to employee turnover as much as possible” via clarifying what metrics are used [see at least Luo [0002-0004] ].
Furthermore, all of the claimed elements were known in the prior arts of a) modified Tsukamoto and b) Luo and c) one skilled in the art could have combined the elements as claimed by known methods with no change in their respective functions, and the combination would have yielded predictable results to one of ordinary skill in the art before the effective filing date of the claimed invention.
Regarding claim 9, modified Tsukamoto teaches the recruitment assistance apparatus according to claim 8,
and Tsukamoto teaches each of a plurality of subject staffs and each of the plurality of departments; and
the at least one processor carries out a recommendation process of deciding, for each of the plurality of subject staffs, a department to be recommended as an acceptance place for that subject staff based on a result of the determination [for the limitations above, see at least [0018, 0072-0073] “According to the prediction device of claim 6, it is possible to predict with high accuracy the degree of activity of a job applicant after joining the company, including the possibility of him or her quitting.
In particular, when using a learning model constructed by the device of claim 2, the personal history data including the history and work history of each existing employee is also taken into account, so that it reflects, for example, the industry, business type, and size of the company, as well as differences in departments, superiors, and colleague environments within the company, making it possible to provide a learning model that is more suited to the actual situation of the company.”;
[0039-0041] “In this specification, "personal career data including history and work history" of existing employees refers to the history and work history of existing employees, as well as data and information regarding, for example, job type, rank (for example, ordinary employee, manager, executive level), assigned department (for example, can be identified by section, division, or business division), assigned superior (for example, can be identified by section manager A, department manager B, business division manager C, or a combination of these), evaluation scores and evaluators from recruitment interviews up to the final interview (such as interview scores).
…
In this specification, "supervised learning" refers to a type of machine learning in which data given in advance is regarded as advice from a teacher and used as a guide for learning.
Good examples of supervised learning include decision trees, random forests, neural networks, deep learning, logistic regression, support vector machines, naive Bayes, boosting, bagging, and proprietary algorithms built by combining these. Of course, it goes without saying that supervised learning methods other than those mentioned above can also be freely adopted.
In this specification, the term "employee applicants" is used in a broad sense.
This includes not only those who literally wish to join a company, but also existing employees who wish to transfer to another department or have not yet been ordered to do so. In other words, other departments in the latter intra-company transfers correspond to the former company. In other words, the purpose is to predict how well an individual will be able to perform in a new environment (a company or other department) if they were to become part of that new environment.”;
[0069-0071] using machine learning to infer candidate placement “Once the necessary data and information have been collected as described above, the evaluation prediction means 55 predicts the post-employment evaluation of each job applicant based on the applicant test data and the optimal prediction model (learning model) (step 25).”;
[0069-0073] describes an after-joining evaluation prediction device, wherein examination data and individual history data of a person who wishes to join a company are acquired, department-by-department evaluation on the person after joining the company is predicted using a learning model that has learned the relationship between the respective examination data and individual history data of multiple existing employees and the respective evaluation data of the existing employees, and the prediction result is displayed “Once the necessary data and information have been collected as described above, the evaluation prediction means 55 predicts the post-employment evaluation of each job applicant based on the applicant test data and the optimal prediction model (learning model) (step 25).
The prediction result is displayed on the display means 9 (step 27). This ends the program. By displaying it on the display means, users (forecasters) who see it can know the predicted evaluation of each applicant after joining the company, and can thereby obtain a useful clue for selecting employees.”].
Modified Tsukamoto doesn’t/don’t explicitly teach but Luo discloses
wherein: in the congeniality determination means process, the at least one processor determines congeniality between each of a plurality of subject staffs and each of the plurality of various users; and
the at least one processor carries out a recommendation process of deciding, for each of the plurality of subject staffs, data to be recommended for that subject staff based on a result of the determination of the congeniality [for the limitations above, see at least [0033] “FIG. 3 depicts an exemplary representation of a job position-to-job position directed graph 300. The job position data in the directed graphs includes both actual job position data and synthetic job position data derived from the actual job position data.”;
[0035] “Referring to FIG. 3, the directed graph 300 depicts an exemplary pair-wise representation of an individual job seeker's three job positions s1, s2, and s3. The job positions s1, s2, and s3 are (job title, company) pairs extracted from the employment history of the job seeker's resumé and the directional ordering is based associated employment dates.;
[0036] where the various job seekers include congenial staff and departments staff “The directed graph 400 consolidates each job seeker's individual directed graph g into a collective job position-to-job position transition directed graph G such as directed graph 400. “G” represents the collective vertices V and edges E of any job position-to-job position directed graph, where g ∈ G. In at least one embodiment, the directed graph G consolidates like vertices and edges from each directed graph g of each job seeker. (Directed graph 300 represents one exemplary embodiment of a directed graph g from a job seeker.) Accordingly, G=(V, E) represents the consolidated directed graphs from sets of job seeker historical job transition data present in actual job data 102 and synthetic data derived therefrom as, for example, subsequently discussed, where V represents the set of all vertices in G, and E represents the set of all edges in G. … For example, if job seeker A has job positions and job position-to-job position transitions of s1→to→s2→s3 and job seeker B has job positions and job position-to-job position transitions of s2→s3→s4, then AI job recommender system 100 creates four unique vertices, one each for job positions s1, s2, s3, and s4 -and weighted edges that reflect two transitions from vertice s2 and one transition from vertices s1 and s3.”;
[0038] match users based similar job paths thus determine similar users “In at least one embodiment, operation 504 represents a quantity of common edges among the job seeker's directed graphs by weighting edges with weights wij representing the frequency of each particular job position-to-job position transitions, i.e. each vertice to vertice transition in the directed graph 400). … In at least one embodiment, in operation 506, AI job recommender system 100 normalizes each weight wij to obtain a job position-to-job position transition (vertice to vertice) transition probability … The process of determining whether a job position (vertice) in one resumé is equivalent to a job position in another resumé is a matter of design choice. In at least one embodiment, AI job recommender system 100 determines two job positions to be similar when job seekers are very likely to move from one job position to another and vice versa (first-order proximity). … Classifying the job position-to-job position transition data for the first job seeker with the optimized, job-to-job transition vector space to predict one or more job transitions for the job seeker in accordance with each transition probability pij so that higher transition probability pij indicate a higher likelihood and higher preference job transition.”].
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify modified Tsukamoto with Luo to include the limitation(s) above as disclosed by Luo. Doing so would help improve modified Tsukamoto’s (Tsukamoto [0004-0006] ) process “to enable recruitment activities to be carried out while predicting the post-employment evaluation of the personnel to be recruited from various angles, and also to eliminate mismatches that may lead to employee turnover as much as possible” via clarifying what metrics are used [see at least Luo [0002-0004] ].
Furthermore, all of the claimed elements were known in the prior arts of a) modified Tsukamoto and b) Luo and c) one skilled in the art could have combined the elements as claimed by known methods with no change in their respective functions, and the combination would have yielded predictable results to one of ordinary skill in the art before the effective filing date of the claimed invention.
Regarding claim 10 (currently amended), Tsukamoto teaches the recruitment assistance apparatus according to claim 1,
and Tsukamoto teaches wherein: the at least one processor further carries out a process of identifying a staff or a department that is at an acceptance place which is likely to accept the subject staff and that has a predetermined relationship with the subject staff,
in the inference process, the at least one processor infers the candidate place of assignment that is of the subject staff and that conforms to the request based on the staff or the department at the acceptance place [for the limitations above, see at least [0018, 0072-0073] “According to the prediction device of claim 6, it is possible to predict with high accuracy the degree of activity of a job applicant after joining the company, including the possibility of him or her quitting.
In particular, when using a learning model constructed by the device of claim 2, the personal history data including the history and work history of each existing employee is also taken into account, so that it reflects, for example, the industry, business type, and size of the company, as well as differences in departments, superiors, and colleague environments within the company, making it possible to provide a learning model that is more suited to the actual situation of the company.”;
[0039-0041] “In this specification, "personal career data including history and work history" of existing employees refers to the history and work history of existing employees, as well as data and information regarding, for example, job type, rank (for example, ordinary employee, manager, executive level), assigned department (for example, can be identified by section, division, or business division), assigned superior (for example, can be identified by section manager A, department manager B, business division manager C, or a combination of these), evaluation scores and evaluators from recruitment interviews up to the final interview (such as interview scores).
…
In this specification, "supervised learning" refers to a type of machine learning in which data given in advance is regarded as advice from a teacher and used as a guide for learning.
Good examples of supervised learning include decision trees, random forests, neural networks, deep learning, logistic regression, support vector machines, naive Bayes, boosting, bagging, and proprietary algorithms built by combining these. Of course, it goes without saying that supervised learning methods other than those mentioned above can also be freely adopted.
In this specification, the term "employee applicants" is used in a broad sense.
This includes not only those who literally wish to join a company, but also existing employees who wish to transfer to another department or have not yet been ordered to do so. In other words, other departments in the latter intra-company transfers correspond to the former company. In other words, the purpose is to predict how well an individual will be able to perform in a new environment (a company or other department) if they were to become part of that new environment.”;
[0069-0071] using machine learning to infer candidate placement “Once the necessary data and information have been collected as described above, the evaluation prediction means 55 predicts the post-employment evaluation of each job applicant based on the applicant test data and the optimal prediction model (learning model) (step 25).”;
[0069-0073] describes an after-joining evaluation prediction device, wherein examination data and individual history data of a person who wishes to join a company are acquired, department-by-department evaluation on the person after joining the company is predicted using a learning model that has learned the relationship between the respective examination data and individual history data of multiple existing employees and the respective evaluation data of the existing employees, and the prediction result is displayed “Once the necessary data and information have been collected as described above, the evaluation prediction means 55 predicts the post-employment evaluation of each job applicant based on the applicant test data and the optimal prediction model (learning model) (step 25).
The prediction result is displayed on the display means 9 (step 27). This ends the program. By displaying it on the display means, users (forecasters) who see it can know the predicted evaluation of each applicant after joining the company, and can thereby obtain a useful clue for selecting employees.”].
Tsukamoto teaches candidate placement recommendation but doesn’t/don’t explicitly teach but Polli discloses
an acceptance place graph, the acceptance place graph including (i) a plurality of nodes each pertaining to the acceptance place, a skill of each of the plurality of persons, or work experience of each of the plurality of persons and (ii) links each indicating a relationship between nodes, the acceptance place graph, [for the limitations above, see at least [0336] “The IoT system may organize the roles of an organization into a role directed graph that may be a tree structure. Each node of the role directed graph may be associated with a role and a model of success indicating suitability for that role that may be based on a set or a subset of games. The link connecting a parent node to a child node may be associated with a criterion for selecting that child node based on the performance on a set or a subset of games (e.g., measurements derived from the set of games of ancestor nodes). A leaf node may be associated with no role indicating that there is no role along the path from the root node to that leaf node. In this case, the system may suggest roles outside the organization based on the candidate's performance on the games played during the assessment for the organization. Rather than indicating no role, a leaf node may indicate one or more other possible organizations or one or more divisions of the organization. Each possible organization or division may be associated with its own role tree structure.”;
[0337] “The IoT system may allow a tree structure to be defined for each organization (e.g., company), each division (e.g., engineering) within an organization, each subsidiary of an organization, and so on.”;
[0338] “FIG. 41 illustrates an example role tree structure for an organization. The role tree structure includes nodes 4101N-4116N and links 4102L-4116L. The role tree structure is associated with an instrument division of an organization. The description within each node represents the role of that node. For example, node 4101N indicates the role of instrument division president, node 4111N indicates the role of human resources manager, node 4112N indicates no role, and node 4114N indicates to next apply the role tree structure of the biotechnology division of the organization. Each node may be associated with a set or a subset of games. Each link may be associated with a criterion for selecting that link, that is determining whether an assessment is to be made of suitability for the role of the node to which the link points—assuming that the candidate is not suitable for the role of the parent node. For example, if a candidate is not suitable to be president of the instrument division, then that candidate may satisfy the criterion associated with links 4102L and 4105L but may not satisfy the criterion associated with links 4103L and 4104L. The use of such criterion allows the playing of the games associated with a child node to be avoided when it is clear that the candidate is not suitable for the role of the child node based on performance on the games associated with the ancestor nodes. In some embodiments, the role directed graph may not be a tree structure because a node may have multiple parent nodes. For example, link 4117L and link 4117L′ indicate node 4110N has two parent node 4102N and node 4104N. If a candidate is determined not to be suitable to be a chief engineer or a marketing manager, then the suitability of the candidate to be an engineering manager may be assessed.”].
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify modified Tsukamoto with Polli to include the limitation(s) above as disclosed by Polli. Doing so would help improve modified Tsukamoto’s (Tsukamoto [0004-0006] ) process “to enable recruitment activities to be carried out while predicting the post-employment evaluation of the personnel to be recruited from various angles, and also to eliminate mismatches that may lead to employee turnover as much as possible” via clarifying what metrics are used [see at least Polli [0002-0004] ].
Furthermore, all of the claimed elements were known in the prior arts of a) modified Tsukamoto and b) Polli and c) one skilled in the art could have combined the elements as claimed by known methods with no change in their respective functions, and the combination would have yielded predictable results to one of ordinary skill in the art before the effective filing date of the claimed invention.
Tsukamoto in view of Polli (Tsukamoto) teaches candidate placement recommendation but doesn’t/don’t explicitly teach however, in the field pertinent to the particular problem with which the applicant was concerned such as similarities between users via graph analysis, Luo discloses
wherein: the at least one processor further carries out a link prediction process of identifying a staff or a department by link prediction using an graph and a subject staff graph including a plurality of nodes pertaining to the subject staff, the graph including (i) a plurality of nodes and (ii) links each indicating a relationship between nodes, and the link prediction being carried out for predicting a relationship between nodes which are not connected to each other by a link in the subject staff graph and the graph,
based on the staff or the department at the acceptance place which has been identified in the link prediction process [for the limitations above, see at least [0033] “FIG. 3 depicts an exemplary representation of a job position-to-job position directed graph 300. The job position data in the directed graphs includes both actual job position data and synthetic job position data derived from the actual job position data.”;
[0035] “Referring to FIG. 3, the directed graph 300 depicts an exemplary pair-wise representation of an individual job seeker's three job positions s1, s2, and s3. The job positions s1, s2, and s3 are (job title, company) pairs extracted from the employment history of the job seeker's resumé and the directional ordering is based associated employment dates.”;
[0036] where the various job seekers include various subject staff and acceptance place staff “The directed graph 400 consolidates each job seeker's individual directed graph g into a collective job position-to-job position transition directed graph G such as directed graph 400. “G” represents the collective vertices V and edges E of any job position-to-job position directed graph, where g ∈ G. In at least one embodiment, the directed graph G consolidates like vertices and edges from each directed graph g of each job seeker. (Directed graph 300 represents one exemplary embodiment of a directed graph g from a job seeker.) Accordingly, G=(V, E) represents the consolidated directed graphs from sets of job seeker historical job transition data present in actual job data 102 and synthetic data derived therefrom as, for example, subsequently discussed, where V represents the set of all vertices in G, and E represents the set of all edges in G. … For example, if job seeker A has job positions and job position-to-job position transitions of s1→to→s2→s3 and job seeker B has job positions and job position-to-job position transitions of s2→s3→s4, then AI job recommender system 100 creates four unique vertices, one each for job positions s1, s2, s3, and s4 -and weighted edges that reflect two transitions from vertice s2 and one transition from vertices s1 and s3.”;
[0038] “In at least one embodiment, operation 504 represents a quantity of common edges among the job seeker's directed graphs by weighting edges with weights wij representing the frequency of each particular job position-to-job position transitions, i.e. each vertice to vertice transition in the directed graph 400). … In at least one embodiment, in operation 506, AI job recommender system 100 normalizes each weight wij to obtain a job position-to-job position transition (vertice to vertice) transition probability … The process of determining whether a job position (vertice) in one resumé is equivalent to a job position in another resumé is a matter of design choice. In at least one embodiment, AI job recommender system 100 determines two job positions to be similar when job seekers are very likely to move from one job position to another and vice versa (first-order proximity). … Classifying the job position-to-job position transition data for the first job seeker with the optimized, job-to-job transition vector space to predict one or more job transitions for the job seeker in accordance with each transition probability pij so that higher transition probability pij indicate a higher likelihood and higher preference job transition.”].
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify Tsukamoto in view of Polli with Luo to include the limitation(s) above as disclosed by Luo. Doing so would help improve modified Tsukamoto in view of Polli’s (Tsukamoto [0004-0006] ) process “to enable recruitment activities to be carried out while predicting the post-employment evaluation of the personnel to be recruited from various angles, and also to eliminate mismatches that may lead to employee turnover as much as possible” via clarifying what metrics are used [see at least Luo [0002-0005] ].
Furthermore, all of the claimed elements were known in the prior arts of a) Tsukamoto in view of Polli and b) Luo and c) one skilled in the art could have combined the elements as claimed by known methods with no change in their respective functions, and the combination would have yielded predictable results to one of ordinary skill in the art before the effective filing date of the claimed invention.
Regarding claim 11 (currently amended), Tsukamoto teaches the recruitment assistance apparatus according to claim 1,
and Tsukamoto teaches an acceptance place, staff indicating a predetermined property, the acceptance place including (i) a plurality of staff each pertaining to an acceptance place which is likely to accept the subject staff, a skill of each of the plurality of persons, or work experience of each of the plurality of persons,
in the inference process, the at least one processor infers the candidate place of assignment that is of the subject staff and that conforms to the request [for the limitations above, see at least [0018, 0072-0073] “According to the prediction device of claim 6, it is possible to predict with high accuracy the degree of activity of a job applicant after joining the company, including the possibility of him or her quitting.
In particular, when using a learning model constructed by the device of claim 2, the personal history data including the history and work history of each existing employee is also taken into account, so that it reflects, for example, the industry, business type, and size of the company, as well as differences in departments, superiors, and colleague environments within the company, making it possible to provide a learning model that is more suited to the actual situation of the company.”;
[0039-0041] “In this specification, "personal career data including history and work history" of existing employees refers to the history and work history of existing employees, as well as data and information regarding, for example, job type, rank (for example, ordinary employee, manager, executive level), assigned department (for example, can be identified by section, division, or business division), assigned superior (for example, can be identified by section manager A, department manager B, business division manager C, or a combination of these), evaluation scores and evaluators from recruitment interviews up to the final interview (such as interview scores).
…
In this specification, "supervised learning" refers to a type of machine learning in which data given in advance is regarded as advice from a teacher and used as a guide for learning.
Good examples of supervised learning include decision trees, random forests, neural networks, deep learning, logistic regression, support vector machines, naive Bayes, boosting, bagging, and proprietary algorithms built by combining these. Of course, it goes without saying that supervised learning methods other than those mentioned above can also be freely adopted.
In this specification, the term "employee applicants" is used in a broad sense.
This includes not only those who literally wish to join a company, but also existing employees who wish to transfer to another department or have not yet been ordered to do so. In other words, other departments in the latter intra-company transfers correspond to the former company. In other words, the purpose is to predict how well an individual will be able to perform in a new environment (a company or other department) if they were to become part of that new environment.”;
[0069-0071] using machine learning to infer candidate placement “Once the necessary data and information have been collected as described above, the evaluation prediction means 55 predicts the post-employment evaluation of each job applicant based on the applicant test data and the optimal prediction model (learning model) (step 25).”;
[0069-0073] describes an after-joining evaluation prediction device, wherein examination data and individual history data of a person who wishes to join a company are acquired, department-by-department evaluation on the person after joining the company is predicted using a learning model that has learned the relationship between the respective examination data and individual history data of multiple existing employees and the respective evaluation data of the existing employees, and the prediction result is displayed “Once the necessary data and information have been collected as described above, the evaluation prediction means 55 predicts the post-employment evaluation of each job applicant based on the applicant test data and the optimal prediction model (learning model) (step 25).
The prediction result is displayed on the display means 9 (step 27). This ends the program. By displaying it on the display means, users (forecasters) who see it can know the predicted evaluation of each applicant after joining the company, and can thereby obtain a useful clue for selecting employees.”].
Tsukamoto teaches candidate placement recommendation but doesn’t/don’t explicitly teach but Polli discloses
an acceptance place graph, the acceptance place graph including (i) a plurality of nodes each pertaining to the acceptance place, a skill of each of the plurality of persons, or work experience of each of the plurality of persons and (ii) links each indicating a relationship between nodes, the acceptance place graph, [for the limitations above, see at least [0336] “The IoT system may organize the roles of an organization into a role directed graph that may be a tree structure. Each node of the role directed graph may be associated with a role and a model of success indicating suitability for that role that may be based on a set or a subset of games. The link connecting a parent node to a child node may be associated with a criterion for selecting that child node based on the performance on a set or a subset of games (e.g., measurements derived from the set of games of ancestor nodes). A leaf node may be associated with no role indicating that there is no role along the path from the root node to that leaf node. In this case, the system may suggest roles outside the organization based on the candidate's performance on the games played during the assessment for the organization. Rather than indicating no role, a leaf node may indicate one or more other possible organizations or one or more divisions of the organization. Each possible organization or division may be associated with its own role tree structure.”;
[0337] “The IoT system may allow a tree structure to be defined for each organization (e.g., company), each division (e.g., engineering) within an organization, each subsidiary of an organization, and so on.”;
[0338] “FIG. 41 illustrates an example role tree structure for an organization. The role tree structure includes nodes 4101N-4116N and links 4102L-4116L. The role tree structure is associated with an instrument division of an organization. The description within each node represents the role of that node. For example, node 4101N indicates the role of instrument division president, node 4111N indicates the role of human resources manager, node 4112N indicates no role, and node 4114N indicates to next apply the role tree structure of the biotechnology division of the organization. Each node may be associated with a set or a subset of games. Each link may be associated with a criterion for selecting that link, that is determining whether an assessment is to be made of suitability for the role of the node to which the link points—assuming that the candidate is not suitable for the role of the parent node. For example, if a candidate is not suitable to be president of the instrument division, then that candidate may satisfy the criterion associated with links 4102L and 4105L but may not satisfy the criterion associated with links 4103L and 4104L. The use of such criterion allows the playing of the games associated with a child node to be avoided when it is clear that the candidate is not suitable for the role of the child node based on performance on the games associated with the ancestor nodes. In some embodiments, the role directed graph may not be a tree structure because a node may have multiple parent nodes. For example, link 4117L and link 4117L′ indicate node 4110N has two parent node 4102N and node 4104N. If a candidate is determined not to be suitable to be a chief engineer or a marketing manager, then the suitability of the candidate to be an engineering manager may be assessed.”].
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify modified Tsukamoto with Polli to include the limitation(s) above as disclosed by Polli. Doing so would help improve modified Tsukamoto’s (Tsukamoto [0004-0006] ) process “to enable recruitment activities to be carried out while predicting the post-employment evaluation of the personnel to be recruited from various angles, and also to eliminate mismatches that may lead to employee turnover as much as possible” via clarifying what metrics are used [see at least Polli [0002-0004] ].
Furthermore, all of the claimed elements were known in the prior arts of a) modified Tsukamoto and b) Polli and c) one skilled in the art could have combined the elements as claimed by known methods with no change in their respective functions, and the combination would have yielded predictable results to one of ordinary skill in the art before the effective filing date of the claimed invention.
Tsukamoto in view of Polli (Tsukamoto) teaches candidate placement recommendation but doesn’t/don’t explicitly teach however, in the field pertinent to the particular problem with which the applicant was concerned such as similarities between users via graph analysis, Luo discloses
calculating, by link prediction using an graph and a subject staff graph, a probability that a node indicating a property links to a node included in the subject staff graph, the subject staff graph including a plurality of nodes pertaining to the subject staff, the graph including (i) a plurality of nodes and (ii) links each indicating a relationship between nodes, and the link prediction being carried out for predicting a relationship between nodes which are not connected to each other by a link in the subject staff graph and the graph,
based on the probability which has been calculated in the link prediction process [for the limitations above, see at least [0033] “FIG. 3 depicts an exemplary representation of a job position-to-job position directed graph 300. The job position data in the directed graphs includes both actual job position data and synthetic job position data derived from the actual job position data.”;
[0035] “Referring to FIG. 3, the directed graph 300 depicts an exemplary pair-wise representation of an individual job seeker's three job positions s1, s2, and s3. The job positions s1, s2, and s3 are (job title, company) pairs extracted from the employment history of the job seeker's resumé and the directional ordering is based associated employment dates.”;
[0036] where the various job seekers include various subject staff and acceptance place staff “The directed graph 400 consolidates each job seeker's individual directed graph g into a collective job position-to-job position transition directed graph G such as directed graph 400. “G” represents the collective vertices V and edges E of any job position-to-job position directed graph, where g ∈ G. In at least one embodiment, the directed graph G consolidates like vertices and edges from each directed graph g of each job seeker. (Directed graph 300 represents one exemplary embodiment of a directed graph g from a job seeker.) Accordingly, G=(V, E) represents the consolidated directed graphs from sets of job seeker historical job transition data present in actual job data 102 and synthetic data derived therefrom as, for example, subsequently discussed, where V represents the set of all vertices in G, and E represents the set of all edges in G. … For example, if job seeker A has job positions and job position-to-job position transitions of s1→to→s2→s3 and job seeker B has job positions and job position-to-job position transitions of s2→s3→s4, then AI job recommender system 100 creates four unique vertices, one each for job positions s1, s2, s3, and s4 -and weighted edges that reflect two transitions from vertice s2 and one transition from vertices s1 and s3.”;
[0038] “In at least one embodiment, operation 504 represents a quantity of common edges among the job seeker's directed graphs by weighting edges with weights wij representing the frequency of each particular job position-to-job position transitions, i.e. each vertice to vertice transition in the directed graph 400). … In at least one embodiment, in operation 506, AI job recommender system 100 normalizes each weight wij to obtain a job position-to-job position transition (vertice to vertice) transition probability … The process of determining whether a job position (vertice) in one resumé is equivalent to a job position in another resumé is a matter of design choice. In at least one embodiment, AI job recommender system 100 determines two job positions to be similar when job seekers are very likely to move from one job position to another and vice versa (first-order proximity). … Classifying the job position-to-job position transition data for the first job seeker with the optimized, job-to-job transition vector space to predict one or more job transitions for the job seeker in accordance with each transition probability pij so that higher transition probability pij indicate a higher likelihood and higher preference job transition.”].
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify Tsukamoto in view of Polli with Luo to include the limitation(s) above as disclosed by Luo. Doing so would help improve modified Tsukamoto in view of Polli’s (Tsukamoto [0004-0006] ) process “to enable recruitment activities to be carried out while predicting the post-employment evaluation of the personnel to be recruited from various angles, and also to eliminate mismatches that may lead to employee turnover as much as possible” via clarifying what metrics are used [see at least Luo [0002-0005] ].
Furthermore, all of the claimed elements were known in the prior arts of a) Tsukamoto in view of Polli and b) Luo and c) one skilled in the art could have combined the elements as claimed by known methods with no change in their respective functions, and the combination would have yielded predictable results to one of ordinary skill in the art before the effective filing date of the claimed invention.
Conclusion
When responding to the office action, any new claims and/or limitations should be accompanied by a reference as to where the new claims and/or limitations are supported in the original disclosure.
Applicant's amendment necessitated the new ground(s) of rejection presented in this Office action. Accordingly, THIS ACTION IS MADE FINAL. See MPEP §706.07(a). Applicant is reminded of the extension of time policy as set forth in 37 CFR 1.136(a).
A shortened statutory period for reply to this final action is set to expire THREE MONTHS from the mailing date of this action. In the event a first reply is filed within TWO MONTHS of the mailing date of this final action and the advisory action is not mailed until after the end of the THREE-MONTH shortened statutory period, then the shortened statutory period will expire on the date the advisory action is mailed, and any extension fee pursuant to 37 CFR 1.136(a) will be calculated from the mailing date of the advisory action. In no event, however, will the statutory period for reply expire later than SIX MONTHS from the date of this final action.
Any inquiry concerning this communication or earlier communications from the examiner should be directed to JAMES WEBB whose telephone number is (313)446-6615. The examiner can normally be reached on M-F 10-3.
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/J.W./Examiner, Art Unit 3624
/Jerry O'Connor/Supervisory Patent Examiner,Group Art Unit 3624