Notice of Pre-AIA or AIA Status
The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA .
DETAILED ACTION
Status of the Application
The following is a Non-Final Office Action in response to communication received on 3/2/2026. Claims 18-25 are pending in this office action.
Continued Examination Under 37 CFR 1.114
A request for continued examination under 37 CFR 1.114, including the fee set forth in 37 CFR 1.17(e), was filed in this application after final rejection. Since this application is eligible for continued examination under 37 CFR 1.114, and the fee set forth in 37 CFR 1.17(e) has been timely paid, the finality of the previous Office action has been withdrawn pursuant to 37 CFR 1.114. Applicant's submission filed on 3/2/2026 has been entered.
Response to Amendment
Applicant’s cancellation of claims 1-17 is acknowledged. Applicant’s amendments to claims 18 and 25 are acknowledged.
Response to Arguments
On Remarks pages 6-7 with respect to the 101 rejection, Applicant argues that the claims do not recite a mental process as the claims cannot be performed in the human mind and cites the USPTO August Memorandum for support of this argument. The Examiner respectfully disagrees. The claims are recited at such a broad level of recitation that the claims recite limitations that can practically be performed in the human mind, including, for example, observations, evaluations, judgments, and opinions.
Specifically, as here in Applicant’s arguments Applicant argues the combination of elements in independent claim 18, it is a mental process to store information in multiple locations, perform linguistics on the information content to determine what content is about, use models or rules to compare information to determine competencies and traits ( where traits are personality traits selected from the group consisting of: openness to experience, conscientiousness, extraversion, agreeableness, empathy, proactiveness, self-determination, and resilience and wherein the competencies are selected from the group consisting of: feeling and deciding, supporting and cooperating, interacting and presenting, analyzing and interpreting, creating and conceptualizing, organizing and executing, adapting and coping, and enterprising and performing), compare the determined competencies and traits to other information to generate scores, to then provide the determined information according to an index or rank. Further it is a mental process as broadly recited in the claims to choose to sequentially operate or review information on different sets of data with the same rules.
The additional argued elements that these mental process steps are performed by “an electric circuit” and “an electronic database”, the information is stored in “a first, second, and third memory” and the text or content is “digital”, the rules are “machine learning”, and the linguistics is performed by “lemmatization” merely results in “apply it.”
Specifically here, with respect to an electric circuit, the different memories, an electronic database, and content being digital, the claim invokes computers or other machinery merely as a tool to perform an existing process. Use of a computer or other machinery in its ordinary capacity for economic or other tasks (e.g. to receive, store or transmit data) or simply adding a general purpose computer or computer components after the fact to an abstract idea does not integrate a judicial exception into a practical application or provide significantly more.
With respect to machine learning and lemmatization the claim recites only the idea of a solution or outcome, i.e. the claim fails to recite details of how a solution to a problem is accomplished. The recitation of claim limitations that attempt to cover any solution to an identified problem with no restriction on how the result is accomplished and no description of the mechanism for accomplishing the result does not integrate a judicial exception into a practical application or provide significantly more because this type of recitation is equivalent to the words apply it. This is the case here as the claims merely recite a result-oriented solution and lack details as to how the computer performed the modifications which is equivalent to the words apply it. Here there are no details about a particular trained machine learning model or lemmatization algorithm other than it being used to determine information. Training is considered part of the abstract idea, as broadly recited in the claims as it is a mental process and certain method of organizing human activity to formulate rules based on previous data. The machine learning or lemmatization are used to generally apply the abstract idea without placing any limitation on how the machine learning or lemmatization operates to derive information. The limitation recites only the idea of using machine learning or lemmatization without details on how this is accomplished. The claim omits any details as to how the machine learning or lemmatization solves a technical problem and instead recites only the idea of a solution or outcome. The claim invokes a generic machine learning or lemmatization merely as a tool for making the calculation or determination rather than purporting to improve the technology or a computer. (additionally see for reference USPTO example 48).
Further, mental process limitations that are instead of being performed at a high of generality by “an electric circuit” and “an electronic database”, the information is stored “a first, second, and third memory”, the text or content is “digital”, the rules are “machine learning”, and the linguistics is “lemmatization” merely results in generally linking the use of the judicial exception to the field of computers.
As MPEP 2106.04(a) makes clear reciting computers at a high level of generality (specifically performing a mental process in a computer environment and using a computer as a tool to perform a mental process) does not prohibit the claims from being within the abstract idea grouping of a mental process, cited herein:
C. A Claim That Requires a Computer May Still Recite a Mental Process
Claims can recite a mental process even if they are claimed as being performed on a computer. The Supreme Court recognized this in Benson, determining that a mathematical algorithm for converting binary coded decimal to pure binary within a computer’s shift register was an abstract idea. The Court concluded that the algorithm could be performed purely mentally even though the claimed procedures "can be carried out in existing computers long in use, no new machinery being necessary." 409 U.S at 67, 175 USPQ at 675. See also Mortgage Grader, 811 F.3d at 1324, 117 USPQ2d at 1699 (concluding that concept of "anonymous loan shopping" recited in a computer system claim is an abstract idea because it could be "performed by humans without a computer").
In evaluating whether a claim that requires a computer recites a mental process, examiners should carefully consider the broadest reasonable interpretation of the claim in light of the specification. For instance, examiners should review the specification to determine if the claimed invention is described as a concept that is performed in the human mind and applicant is merely claiming that concept performed 1) on a generic computer, or 2) in a computer environment, or 3) is merely using a computer as a tool to perform the concept. In these situations, the claim is considered to recite a mental process.
1. Performing a mental process on a generic computer. An example of a case identifying a mental process performed on a generic computer as an abstract idea is Voter Verified, Inc. v. Election Systems & Software, LLC, 887 F.3d 1376, 1385, 126 USPQ2d 1498, 1504 (Fed. Cir. 2018). In this case, the Federal Circuit relied upon the specification in explaining that the claimed steps of voting, verifying the vote, and submitting the vote for tabulation are "human cognitive actions" that humans have performed for hundreds of years. The claims therefore recited an abstract idea, despite the fact that the claimed voting steps were performed on a computer. 887 F.3d at 1385, 126 USPQ2d at 1504. Another example is Versata, in which the patentee claimed a system and method for determining a price of a product offered to a purchasing organization that was implemented using general purpose computer hardware. 793 F.3d at 1312-13, 1331, 115 USPQ2d at 1685, 1699. The Federal Circuit acknowledged that the claims were performed on a generic computer, but still described the claims as "directed to the abstract idea of determining a price, using organizational and product group hierarchies, in the same way that the claims in Alice were directed to the abstract idea of intermediated settlement, and the claims in Bilski were directed to the abstract idea of risk hedging." 793 F.3d at 1333; 115 USPQ2d at 1700-01.
2. Performing a mental process in a computer environment. An example of a case identifying a mental process performed in a computer environment as an abstract idea is Symantec Corp., 838 F.3d at 1316-18, 120 USPQ2d at 1360. In this case, the Federal Circuit relied upon the specification when explaining that the claimed electronic post office, which recited limitations describing how the system would receive, screen and distribute email on a computer network, was analogous to how a person decides whether to read or dispose of a particular piece of mail and that "with the exception of generic computer-implemented steps, there is nothing in the claims themselves that foreclose them from being performed by a human, mentally or with pen and paper". 838 F.3d at 1318, 120 USPQ2d at 1360. Another example is FairWarning IP, LLC v. Iatric Sys., Inc., 839 F.3d 1089, 120 USPQ2d 1293 (Fed. Cir. 2016). The patentee in FairWarning claimed a system and method of detecting fraud and/or misuse in a computer environment, in which information regarding accesses of a patient’s personal health information was analyzed according to one of several rules (i.e., related to accesses in excess of a specific volume, accesses during a pre-determined time interval, or accesses by a specific user) to determine if the activity indicates improper access. 839 F.3d. at 1092, 120 USPQ2d at 1294. The court determined that these claims were directed to a mental process of detecting misuse, and that the claimed rules here were "the same questions (though perhaps phrased with different words) that humans in analogous situations detecting fraud have asked for decades, if not centuries." 839 F.3d. at 1094-95, 120 USPQ2d at 1296.
3. Using a computer as a tool to perform a mental process. An example of a case in which a computer was used as a tool to perform a mental process is Mortgage Grader, 811 F.3d. at 1324, 117 USPQ2d at 1699. The patentee in Mortgage Grader claimed a computer-implemented system for enabling borrowers to anonymously shop for loan packages offered by a plurality of lenders, comprising a database that stores loan package data from the lenders, and a computer system providing an interface and a grading module. The interface prompts a borrower to enter personal information, which the grading module uses to calculate the borrower’s credit grading, and allows the borrower to identify and compare loan packages in the database using the credit grading. 811 F.3d. at 1318, 117 USPQ2d at 1695. The Federal Circuit determined that these claims were directed to the concept of "anonymous loan shopping", which was a concept that could be "performed by humans without a computer." 811 F.3d. at 1324, 117 USPQ2d at 1699. Another example is Berkheimer v. HP, Inc., 881 F.3d 1360, 125 USPQ2d 1649 (Fed. Cir. 2018), in which the patentee claimed methods for parsing and evaluating data using a computer processing system. The Federal Circuit determined that these claims were directed to mental processes of parsing and comparing data, because the steps were recited at a high level of generality and merely used computers as a tool to perform the processes. 881 F.3d at 1366, 125 USPQ2d at 1652-53.
On Remarks pages 7-8, Applicant argues that the claims do not make an employment or education determination and therefore the claims do not recite certain method of organizing human activities. The Examiner strongly disagrees.
Claim 19 clearly states that the process is for employment opportunity or education programs. Therefore the Examiner strongly disagrees.
Further it is noted as recited in MPEP 2106.04 a claim can recite a judicial exception without explicitly disclosing the words in the claims, cited herein:
Prong One asks does the claim recite an abstract idea, law of nature, or natural phenomenon? In Prong One examiners evaluate whether the claim recites a judicial exception, i.e. whether a law of nature, natural phenomenon, or abstract idea is set forth or described in the claim. While the terms "set forth" and "described" are thus both equated with "recite", their different language is intended to indicate that there are two ways in which an exception can be recited in a claim. For instance, the claims in Diehr, 450 U.S. at 178 n. 2, 179 n.5, 191-92, 209 USPQ at 4-5 (1981), clearly stated a mathematical equation in the repetitively calculating step, and the claims in Mayo, 566 U.S. 66, 75-77, 101 USPQ2d 1961, 1967-68 (2012), clearly stated laws of nature in the wherein clause, such that the claims "set forth" an identifiable judicial exception. Alternatively, the claims in Alice Corp., 573 U.S. at 218, 110 USPQ2d at 1982, described the concept of intermediated settlement without ever explicitly using the words "intermediated" or "settlement."
On Remarks pages 8-10 with respect to the 101 rejection, Applicant argues “the claim reflect a specific technical improvement over position specific machine learning models”. The Examiner has carefully considered Applicant’s arguments however the Examiner maintains the position from the previous office action.
Specifically from the Final Office Action dated 12/5/2025 on pages 3-9 (cited herein):
“Applicant argues the claimed invention reflects an improvement of the claimed electronic circuit over the prior art systems based on Applicant's specification at paragraph 0009. The Examiner has carefully considered Applicant's arguments here. However, the arguments are not persuasive. While it is true, Applicant's specification does suggest or state an improvement, specifically "These benchmark values inform the evaluator as to the importance of particular traits and competencies but eliminate the need for a large number of machine learning systems trained for each position. Providing guidance to the evaluate in the form of benchmark scores also provides transparency to the process that would not be obtained in a pure machine learning model" such stated or suggested improvements are not reflected in Applicant's claims as amended or recited, therefore not an improvement to the functioning of a computer or to any other technology or technical field (see MPEP 2106.05(a)).
Here Applicant does not recite the mechanism or how this improvement is
performed in the claims. Rather Applicant's claims are broad and require the opposite
of the improvement recited in paragraph 0009. Specifically, the limitation of "a set of
machine learning systems communicating with the first and second data memories each
machine learning system having a unique model trained to evaluate characteristics of
text records to provide corresponding competencies and trait scores" require multiple
models of training for each the first and second data which each includes position
information, which therefore require machine learning models training each position that
is claimed.
Further Examiner has reviewed the specification and the cited sections, but does
not find where in the specification the specific machine learning or other computer
mechanism for implementing this argued or suggested improvement discussed in paragraph 0009 is found, therefore this remains at the "apply it" level (see MPEP
2106.05 (f), Mere Instructions To Apply An Exception [R-10.2019] cited herein:
When determining whether a claim simply recites a judicial exception with
the words "apply it" (or an equivalent), such as mere instructions to implement
an abstract idea on a computer, examiners may consider the following:
(1) Whether the claim recites only the idea of a solution or
outcome i.e., the claim fails to recite details of how a solution to a problem
is accomplished. The recitation of claim limitations that attempt to cover any
solution to an identified problem with no restriction on how the result is
accomplished and no description of the mechanism for accomplishing the result,
does not integrate a judicial exception into a practical application or provide
significantly more because this type of recitation is equivalent to the words
"apply it". See Electric Power Group, LLC v. Alstom, S.A., 830 F.3d 1350, 1356,
119 USPQ2d 1739, 17 43-44 (Fed. Cir. 2016); Intellectual Ventures Iv.
Symantec, 838 F.3d 1307, 1327, 120 USPQ2d 1353, 1366 (Fed. Cir.
2016); Internet Patents Corp. v. Active Network, Inc., 790 F .3d 1343, 1348, 115
USPQ2d 1414, 1417 (Fed. Cir. 2015). In contrast, claiming a particular solution
to a problem or a particular way to achieve a desired outcome may integrate the
judicial exception into a practical application or provide significantly more.
See Electric Power, 830 F.3d at 1356, 119 USPQ2d at 1743.
By way of example, in Intellectual Ventures Iv. Capital One Fin.
Corp., 850 F.3d 1332, 121 USPQ2d 1940 (Fed. Cir. 2017), the steps in the
claims described "the creation of a dynamic document based upon 'management
record types' and 'primary record types."' 850 F.3d at 1339-40; 121 USPQ2d at
1945-46. The claims were found to be directed to the abstract idea of "collecting,
displaying, and manipulating data." 850 F.3d at 1340; 121 USPQ2d at 1946. In
addition to the abstract idea, the claims also recited the additional element of
modifying the underlying XML document in response to modifications made in
the dynamic document. 850 F.3d at 1342; 121 USPQ2d at 1947-48. Although the
claims purported to modify the underlying XML document in response to
modifications made in the dynamic document, nothing in the claims indicated
what specific steps were undertaken other than merely using the abstract idea in
the context of XML documents. The court thus held the claims ineligible, because
the additional limitations provided only a result-oriented solution and lacked
details as to how the computer performed the modifications, which was
equivalent to the words "apply it". 850 F.3d at 1341-42; 121 USPQ2d at 1947-48
(citing Electric Power Group., 830 F.3d at 1356, 1356, USPQ2d at 17 43-44
(cautioning against claims "so result focused, so functional, as to effectively
cover any solution to an identified problem")).
Other examples where the courts have found the additional elements to
be mere instructions to apply an exception, because they recite no more than an
idea of a solution or outcome include:
• i. Remotely accessing user-specific information through a mobile interface and
pointers to retrieve the information without any description of how the mobile
interface and pointers accomplish the result of retrieving previously inaccessible
information, Intellectual Ventures v. Erie lndem. Co., 850 F.3d 1315, 1331, 121
USPQ2d 1928, 1939 (Fed. Cir. 2017);
• ii. A general method of screening emails on a generic computer without any
limitations that addressed the issues of shrinking the protection gap and mooting
the volume problem, Intellectual Ventures Iv. Symantec Corp., 838 F.3d 1307,
1319, 120 USPQ2d 1353, 1361 (Fed. Cir. 2016); and
• iii. Wireless delivery of out-of-region broadcasting content to a cellular telephone
via a network without any details of how the delivery is accomplished, Affinity
Labs of Texas v. DirecTV, LLC, 838 F.3d 1253, 1262-63, 120 USPQ2d 1201,
1207 (Fed. Cir. 2016).
In contrast, other cases have found that additional elements are more than
"apply it" or are not "mere instructions" when the claim recites a technological
solution to a technological problem. In DOR Holdings, the court found that the
additional elements did amount to more than merely instructing that the abstract
idea should be applied on the Internet. DOR Holdings, LLC v. Hotels.com,
L.P., 773 F.3d 1245, 1259, 113 USPQ2d 1097, 1107 (Fed. Cir. 2014). The claims
at issue specified how interactions with the Internet were manipulated to yield a
desired result-a result that overrode the routine and conventional sequence of
events ordinarily triggered by the click of a hyperlink. 773 F.3d at 1258; 113
USPQ2d at 1106. In BASCOM, the court determined that the claimed
combination of limitations did not simply recite an instruction to apply the abstract
idea of filtering content on the Internet. BASCOM Global Internet Servs. v. AT&T
Mobility, LLC, 827 F.3d 1341, 1350, 119 USPQ2d 1236, 1243 (Fed. Cir. 2016).
Instead, the claim recited a "technology based solution" of filtering content on the
Internet that overcome the disadvantages of prior art filtering systems. 827 F.3d
at 1350-51, 119 USPQ2d at 1243. Finally, in Thales Visionix, the particular
configuration of inertial sensors and the particular method of using the raw data
from the sensors was more than simply applying a law of nature. Thales Visionix,
Inc. v. United States, 850 F.3d 1343, 1348-49, 121 USPQ2d 1898, 1902 (Fed.
Cir. 2017). The court found that the claims provided a system and method that
"eliminate[d] many 'complications' inherent in previous solutions for determining
position and orientation of an object on a moving platform." In other words, the
claim recited a technological solution to a technological problem. Id.
Additionally see, MPEP 2106.05 (a) Improvements to the Functioning of a Computer or To Any Other Technology or Technical Field [R-07.2022]:
If it is asserted that the invention improves upon conventional functioning of a
computer, or upon conventional technology or technological processes, a technical explanation as to how to implement the invention should be present in the specification.
That is, the disclosure must provide sufficient details such that one of ordinary skill in
the art would recognize the claimed invention as providing an improvement. The
specification need not explicitly set forth the improvement, but it must describe the
invention such that the improvement would be apparent to one of ordinary skill in the
art. Conversely, if the specification explicitly sets forth an improvement but in a
conclusory manner (i.e., a bare assertion of an improvement without the detail
necessary to be apparent to a person of ordinary skill in the art), the examiner should
not determine the claim improves technology. An indication that the claimed invention
provides an improvement can include a discussion in the specification that identifies a
technical problem and explains the details of an unconventional technical solution
expressed in the claim, or identifies technical improvements realized by the claim over
the prior art.”
Applicant argues further paragraphs 0050 and 0073 (as published) for support of this argued improvement and in response to Examiner’s remarks, cited herein above:
Paragraph 0050- A wide variety of analysis engines 62 may be used including machine learning models, natural language processing models, neural networks, or the like. Example analysis engines are described in U.S. patent application Ser. No. 16/847,348 filed Apr. 13, 2020, entitled: “System and Method for Evaluating Employment Candidates or Employees,” assigned to the assignee of the present application and hereby incorporated by reference. The different output metrics produced by the analysis engine 62 may include personality traits and competencies.
Paragraph 0073- The output metrics of the analysis engines 62 are then linked to corresponding benchmark metrics 63 from the database 44 associated with the selected occupation 59. Generally, the corresponding benchmark metrics 63 are previously developed from the outputs of analysis engines 62 working on the data files 56 of other individuals associated with the selected occupation 59. In one embodiment, the benchmark metrics 63 measure people who have self-selected as being interested in that occupation whether they are ultimately hired or not and regardless of their success. As will be understood, this provides evaluator 16 with a sense of the potential pool of applicants and can be expressed, for example, by a number of statistical measures, for example, a median value or average value. Alternatively, or in addition, the benchmark metric 63 may be limited to individuals who have had a positive post-interview experience, for example, a call back for a second interview or a positive hiring decision or post-hiring activity such as evaluations by supervisors, longevity in the job, sales metrics, or the like. Generally, this information may be obtained empirically as will be discussed below, but initially it may be prepared using skilled individuals making a predictive assessment of necessary characteristics.
These citations provide no further guidance (details on manner in which the invention accomplishes the alleged improvement in machine learning from Applicant’s specification at paragraph 0009 of ““These benchmark values inform the evaluator as to the importance of particular traits and competencies but eliminate the need for a large number of machine learning systems trained for each position. Providing guidance to the evaluator in the form of benchmark scores also provides transparency to the process that would not be obtained in a pure machine learning model” , see MPEP 2106.05(a), cited herein:
If it is asserted that the invention improves upon conventional functioning of a computer, or upon conventional technology or technological processes, a technical explanation as to how to implement the invention should be present in the specification. That is, the disclosure must provide sufficient details such that one of ordinary skill in the art would recognize the claimed invention as providing an improvement. The specification need not explicitly set forth the improvement, but it must describe the invention such that the improvement would be apparent to one of ordinary skill in the art. Conversely, if the specification explicitly sets forth an improvement but in a conclusory manner (i.e., a bare assertion of an improvement without the detail necessary to be apparent to a person of ordinary skill in the art), the examiner should not determine the claim improves technology. An indication that the claimed invention provides an improvement can include a discussion in the specification that identifies a technical problem and explains the details of an unconventional technical solution expressed in the claim, or identifies technical improvements realized by the claim over the prior art. For example, in McRO, the court relied on the specification’s explanation of how the particular rules recited in the claim enabled the automation of specific animation tasks that previously could only be performed subjectively by humans, when determining that the claims were directed to improvements in computer animation instead of an abstract idea. McRO, 837 F.3d at 1313-14, 120 USPQ2d at 1100-01. In contrast, the court in Affinity Labs of Tex. v. DirecTV, LLC relied on the specification’s failure to provide details regarding the manner in which the invention accomplished the alleged improvement when holding the claimed methods of delivering broadcast content to cellphones ineligible. 838 F.3d 1253, 1263-64, 120 USPQ2d 1201, 1207-08 (Fed. Cir. 2016).
Rather again given the broad claim language as claimed, the claim scope is so broad it actually additionally encompasses the claim scope of what is asserted in the specification at paragraph 0009 that it is an improvement to, specifically the claim scope includes using multiple trained models to determine the target position. This means the claim scope includes the interpretation of the opposite of what is actually claimed as an improvement in paragraph 0009, specifically using multiple machine learning models each trained to determine the target position (specifically as recited in the claim language of “a set of machine learning systems communicating with the first and second data memories each machine learning system having a unique model trained to evaluate characteristics of text records to provide corresponding competencies and trait scores…wherein the electronic circuit operates sequentially on the first and second set of digital text records using a set of same models to output competency and trait scores from the second set of digital text records together with competency and trait scores from the first set of digital records indexed by the target position from the electronic database to reduce effects of bias in the models”) (emphasis added). This is further evidenced by claim 22 that recite that target position use a same set of machine learning systems (“…and target position use a same predetermined set of interview questions and a same set of machine learning systems.”)(emphasis added).
Further additionally or alternatively, this also given the broad recited claim language encompasses claim scope of the opposite of the asserted improvement in argued paragraph 0009 as there is still needed a large number of models to be trained for each position, as the claims and the specification as argued require a unique trained machine learning model for every competency or trait, therefore if the system is scoring multiple traits or competencies to determine a target position, as amended in claim 18 the different traits and competencies can be numerous, the claims could still require a large number of models trained for each position (as the above referenced trained models are used to determine results of the target position as claimed), resulting in the opposite of what is actually asserted in paragraph 0009 as the improvement.
Therefore when considering all the facts, evidence presented by the specification, and analysis above, the Examiner maintains that while the specification may suggest or assert an improvement this is but in a conclusory manner without the detail necessary to be apparent to one of ordinary skill in the art nor is this reflected in the claims as required, see MPEP 2106.05(a) cited herein:
If it is asserted that the invention improves upon conventional functioning of a computer, or upon conventional technology or technological processes, a technical explanation as to how to implement the invention should be present in the specification. That is, the disclosure must provide sufficient details such that one of ordinary skill in the art would recognize the claimed invention as providing an improvement. The specification need not explicitly set forth the improvement, but it must describe the invention such that the improvement would be apparent to one of ordinary skill in the art. Conversely, if the specification explicitly sets forth an improvement but in a conclusory manner (i.e., a bare assertion of an improvement without the detail necessary to be apparent to a person of ordinary skill in the art), the examiner should not determine the claim improves technology. An indication that the claimed invention provides an improvement can include a discussion in the specification that identifies a technical problem and explains the details of an unconventional technical solution expressed in the claim, or identifies technical improvements realized by the claim over the prior art. For example, in McRO, the court relied on the specification’s explanation of how the particular rules recited in the claim enabled the automation of specific animation tasks that previously could only be performed subjectively by humans, when determining that the claims were directed to improvements in computer animation instead of an abstract idea. McRO, 837 F.3d at 1313-14, 120 USPQ2d at 1100-01. In contrast, the court in Affinity Labs of Tex. v. DirecTV, LLC relied on the specification’s failure to provide details regarding the manner in which the invention accomplished the alleged improvement when holding the claimed methods of delivering broadcast content to cellphones ineligible. 838 F.3d 1253, 1263-64, 120 USPQ2d 1201, 1207-08 (Fed. Cir. 2016).
After the examiner has consulted the specification and determined that the disclosed invention improves technology, the claim must be evaluated to ensure the claim itself reflects the disclosed improvement in technology. Intellectual Ventures I LLC v. Symantec Corp., 838 F.3d 1307, 1316, 120 USPQ2d 1353, 1359 (Fed. Cir. 2016) (patent owner argued that the claimed email filtering system improved technology by shrinking the protection gap and mooting the volume problem, but the court disagreed because the claims themselves did not have any limitations that addressed these issues). That is, the claim must include the components or steps of the invention that provide the improvement described in the specification. However, the claim itself does not need to explicitly recite the improvement described in the specification (e.g., "thereby increasing the bandwidth of the channel"). The full scope of the claim under the BRI should be considered to determine if the claim reflects an improvement in technology (e.g., the improvement described in the specification). In making this determination, it is critical that examiners look at the claim "as a whole," in other words, the claim should be evaluated "as an ordered combination, without ignoring the requirements of the individual steps." When performing this evaluation, examiners should be "careful to avoid oversimplifying the claims" by looking at them generally and failing to account for the specific requirements of the claims. McRO, 837 F.3d at 1313, 120 USPQ2d at 1100.
An important consideration in determining whether a claim improves technology is the extent to which the claim covers a particular solution to a problem or a particular way to achieve a desired outcome, as opposed to merely claiming the idea of a solution or outcome. McRO, 837 F.3d at 1314-15, 120 USPQ2d at 1102-03; DDR Holdings, 773 F.3d at 1259, 113 USPQ2d at 1107. In this respect, the improvement consideration overlaps with other considerations, specifically the particular machine consideration (see MPEP § 2106.05(b)), and the mere instructions to apply an exception consideration (see MPEP § 2106.05(f)). Thus, evaluation of those other considerations may assist examiners in making a determination of whether a claim satisfies the improvement consideration.
Therefore based on the above, the Examiner respectfully disagrees.
On remarks page 10, Applicant argues that the claims recite more than mere instructions to apply the exception. The Examiner respectfully disagrees. Specifically with respect to without returning retraining per position, the Examiner respectfully disagrees based on 5 above and 8 above. With respect to NLP based dictionary files, TF-SID calculations such limitations are not recited or required by the claims therefore the Examiner finds such arguments not persuasive. As to Applicant’s regarding circuits, these have been addressed below under the additional elements citations. As to Applicant’s arguments above linking output metrics to benchmark data this is part of the abstract idea specifically compare the determined competencies and traits to other information to generate scores, to then provide the determined information according to an index or rank. Therefore, this argument is not persuasive as this is found in the abstract idea.
On Remarks pages 5-7, Applicant argues the claims recite an inventive concept. Here Applicant argues Desjardins, an improvement in not requiring retraining and cites paragraphs 0007, 0009, 0049, 0050, 0072-0074 for support of this argument. It is first noted that Applicant’s specification does not disclose or discuss retraining or improvements thereto. Therefore, the argument is not persuasive.
The Examiner interprets Applicant is arguing here the asserted improvement previously addressed above in response to arguments section 5 of “"These benchmark values inform the evaluator as to the importance of particular traits and competencies but eliminate the need for a large number of machine learning systems trained for each position. Providing guidance to the evaluate in the form of benchmark scores also provides transparency to the process that would not be
obtained in a pure machine learning model."
The Examiner maintains the previous response above, as Desjardins was with respect to improvements to the functioning of a computer (which was previously addressed above in section 5) as discussed in the USPTO Memorandum dated 12/5/2025 Advance notice of change to the MPEP in light of Ex Parte Desjardins (cited herein):
This advance notice revises the Ninth Edition, Revision 01.2024, November 2024 publication of the Manual of Patent Examining Procedure (MPEP) to include Ex Parte Desjardins, as indicated below. These updates are not intended to announce any new USPTO practice or procedure and are meant to be consistent with existing USPTO guidance. Indeed, the Ex Parte Desjardins decision analyzed eligibility in terms of whether the claims were directed to an improvement in the functioning of a computer, or an improvement to other technology or technical field under longstanding Federal Circuit precedent in Enfish, LLC v. Microsoft Corp., 822 F.3d 1327 (Fed. Cir. 2016) and McRO, Inc. v. Bandai Namco Games Am. Inc., 837 F.3d 1299 (Fed. Cir. 2016). See also MPEP §§ 2106.04(d)(l) and 2106.05(a).
Specifically, Ex Parte Desjardins explained the following:
Enfish ranks among the Federal Circuit's leading cases on the eligibility of technological improvements. In particular, Enfish recognized that “[m]uch of the advancement made in computer technology consists of improvements to software that, by their very nature, may not be defined by particular physical features but rather by logical structures and processes.” 822 F.3d at 1339. Moreover, because “[s]oftware can make non-abstract improvements to computer technology, just as hardware improvements can,” the Federal Circuit held that the eligibility determinations should turn on whether “the claims are directed to an improvement to computer functionality versus being directed to an abstract idea.” Id. at 1336. (Desjardins, page 8).
As such, Examiners are expected to consider existing precedent like Enfish, as discussed in MPEP § 2106, in addition to these updates when assessing eligibility under 35 U.S.C. § 101, particularly when evaluating claims related to machine learning or artificial intelligence.
On Remarks page 12, Applicant argues the 112 rejections, in view of Applicant’s amendments and arguments the previously 112 rejections have been withdrawn.
On Remarks page 13-14, Applicant argues the 103 rejection. In particular Applicant argues Applicant’s amendments. It is first noted this amendment is not reflected in independent claim 25 therefore the arguments are not persuasive with respect to claim 25.
With respect to claim 18, Ghosh teaches a system of using a computer system to find a job candidate similar to another previous employee who held a job (see paragraph 0017 and 0026). This is done through comparing in part the candidate to the previous user to determine related attributes and ranking candidates using such information to determine if a candidate is suitable for a job (see paragraphs 0033, 0036, and 0042).
Ghosh goes on to teach that the system can compare candidate responses to interview questions or job simulations in an interview to previous employees who engaged in the same or similar process (question or simulation) to find a match (see paragraph 0049). Ghosh goes to teach that these responses can be “frustrated, fearful, sad, satisfied, excited, polite, impolite, sympathetic, angry, analytical” (see paragraphs 0047 and 0059).
Applicant’s amended broad claim language to do not recite a specific way or ways each or even one of these different categories are calculated. Rather the claims merely define the different traits or competencies in broad category labels. It is further noted that the claims are recited in the Markush format, and therefore only require selection (e.g. one) from the set of alternative members. As these are labels defining broad categories (with no explanation in the claims as to how such categories are calculated or formed), it stands to reason that a user who is labeled as “sad” for an question or simulation for an interview could be grouped into different categories of these broad labels like “openness to experience”, “resilience”, or “self-determination” for example. Alternatively for an example a user who is “polite” may fall into the “agreeableness” category and so forth. Further a user who is “analytical” could fall into the “analyzing and interpreting” just as a user who is “polite” could fall into the “supporting and cooperating” broad category definitions.
Therefore, the Examiner still interprets the teachings of Ghosh to read on the claims under broadest reasonable interpretation given the plain meaning of these broad category labels as would be understood by one of ordinary skill in the art.
In the efforts of compact prosecution, if Applicant were to further define the broad category labels of traits and competencies, Applicant’s attention is pointed of newly cited prior art of Baldwin et al. (United States Patent Number: US 7,502,748, currently not relied upon to teach the pending claims) which teaches a system for matching employment candidates with employment positions based on personality traits and additional teaches using information from users currently filling positions (benchmark users) to determine suitable employees for each position (see abstract) and further teaches traits include things like conscientiousness, organization, extraversion, group orientation, stability, poise, social desirability (see Figure 3).
Claim Rejections - 35 USC § 101
11. 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.
12. Claims 18-25 are rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more.
Claims 18-24 recite a machine as the claims recite an electronic circuit with memory and a database. Claim 25 recites a process as the claims recite a method.
The claim(s) 18-25 recite(s) collecting and storing information related to potential employees and people who were offered employment including interview information, analyzing the information according to rules to generate scores, to then make an employment or education program determination.
The claims are recited at such a high level of generality they recite limitations a human or humans could practically perform in the human mind or with a physical aids (like pen and paper) as the claims recite observations, evaluations, judgements and opinions.
Further the claims recite business relations as the relate to making employment or educational programs decisions which is a commercial or legal interaction. Further commercial or legal interactions are a certain methods of organizing human activities.
Mental processes as well as certain methods of organizing human activities are in the groupings of enumerated abstracts ideas, and hence the claims recite an abstract idea.
This judicial exception is not integrated into a practical application because the claims merely recite limitations that are not indicative of integration into a practical application in that the claims merely recite:
(1) Adding the words “apply it” ( or an equivalent) with the judicial exception, or mere instructions to implement an abstract idea on a computer, or merely uses a computer as a tool to perform an abstract idea (see MPEP 2106.05(f)), and (2) Generally linking the use of the judicial exception to a particular technological environment or field of use (see MPEP 2106.05(h)).
Specifically as recited in the claims:
The Examiner notes that the Examiner has underlined and bolded additional limitations. Limitations that are not bolded and underlined are considered part of the abstract idea.
18. An electronic circuit comprising:
a first data memory holding multiple first sets of multiple digital text records, each different first set representing data from benchmark individuals of a given benchmark position;
a second data memory holding a second set of digital text records representing an evaluation individual for a target position;
a third data memory holding a target position;
a preprocessor providing a lemmantization to the data of the first and second data memories;
a set of machine learning systems communicating with the first and second data memories each machine learning system having a unique model trained to evaluate characteristics of text records to provide corresponding competencies and traits scores;
an electronic database communicating with the first data memory to receive competency and trait scores indexed according to the benchmark positions and communicating with the third data memory to receive the target position and to output competency and trait scores according to an indexing by the target position;
wherein the electronic circuit operates sequentially on the first and second set of digital text records using a set of same models to output competency and trait scores from the second set of digital text records together with competency and trait scores from the first set of digital records indexed by the target position from the electronic database to reduce the effects of bias in the models;
wherein the traits are personality traits selected from the group consisting of: openness to experience, conscientiousness, extraversion, agreeableness, empathy, proactiveness, self-determination, and resilience; and
wherein the competencies are selected from the group consisting of: feeling and deciding, supporting and cooperating, interacting and presenting, analyzing and interpreting, creating and conceptualizing, organizing and executing, adapting and coping, and enterprising and performing.
19. The circuit of claim 18 wherein the target position and benchmark position matching the target position are selected from the group consisting of an employment opportunity and an educational program.
20. The circuit of claim 18 wherein the electronic database further receives the competency and trait scores from the first data memory from multiple benchmark individuals and statistically combines them.
21. The circuit of claim 20 wherein the benchmark individuals are individuals to whom a given benchmark position was offered.
22. The circuit of claim 18 wherein the digital text records include responses to a predetermined set of interview questions, and wherein the text records associated with each given corresponding benchmark position and target position use a same predetermined set of interview questions and a same set of machine learning systems.
23. The circuit of claim 18 wherein the electronic circuit outputs a comparison with a quantitative representation of the benchmark scores.
24. The circuit of claim 18 wherein the benchmark and target positions in the database include position categories and position subcategories arranged within the position categories.
25. A method of operating an electronic circuit, the method comprising:
holding, in a first data memory of the electronic circuit, multiple first sets of digital text records, each different first set representing data from benchmark individuals of a given benchmark position;
holding, in a second data memory of the electronic circuit, a second set of digital text records representing an evaluation individual for a target position;
holding, in a third data memory of the electronic circuit, data representing the target position;
providing, by a preprocessor of the electronic circuit, the digital text records of the first and second data memories by performing lemmatization;
communicating, by a set of machine learning systems of the electronic circuit, with the first and second data memories each machine learning system having a unique model trained to evaluate characteristics of text records to generate corresponding competency and trait scores;
and communicating, by an electronic database of the electronic circuit, with the first data memory to receive competency and trait scores indexed according to the benchmark positions and communicating with the third data memory to receive the target position and to output competency and trait scores according to an indexing by the target position;
wherein, by the electronic circuit, sequentially operating on the first and second set of digital text records using a same models to output competency and trade scores from the second set of digital text records together with competency and trait scores from the first set of digital records indexed by the target position from the electronic database to reduce effects of bias in the models.
As per claim 18, the claims recite mental process and certain methods of organizing human activities. Specifically it is a mental process and certain methods of organizing human activities to store information in multiple locations, perform linguistics on the information content to determine what content is about, use models or rules to compare information to determine competencies and traits ( where categories (labels) of traits are personality traits selected from the group consisting of: openness to experience, conscientiousness, extraversion, agreeableness, empathy, proactiveness, self-determination, and resilience and wherein categories (labels) of the competencies are selected from the group consisting of: feeling and deciding, supporting and cooperating, interacting and presenting, analyzing and interpreting, creating and conceptualizing, organizing and executing, adapting and coping, and enterprising and performing), compare the determined competencies and traits to other information to generate scores, to then provide the determined information according to an index or rank. Further it is a mental process and certain methods of organizing human activity as broadly recited in the claims to choose to sequentially operate or review information on different sets of data with the same rules. This is part of the abstract idea.
The additional elements that these mental process or certain method of organizing human activities are performed by “an electric circuit” and “an electronic database”, the information is stored in “a first, second, and third memory” and the text or content is “digital”, the rules are “machine learning”, and the linguistics is “lemmatization” merely results in “apply it.”
Specifically here, with respect to an electric circuit, the different memories, an electronic database, and content being digital, the claim invokes computers or other machinery merely as a tool to perform an existing process. Use of a computer or other machinery in its ordinary capacity for economic or other tasks (e.g. to receive, store or transmit data) or simply adding a general purpose computer or computer components after the fact to an abstract idea does not integrate a judicial exception into a practical application or provide significantly more.
With respect to machine learning and lemmatization the claim recites only the idea of a solution or outcome, i.e. the claim fails to recite details of how a solution to a problem is accomplished. The recitation of claim limitations that attempt to cover any solution to an identified problem with no restriction on how the result is accomplished and no description of the mechanism for accomplishing the result does not integrate a judicial exception into a practical application or provide significantly more because this type of recitation is equivalent to the words apply it. This is the case here as the claims merely recite a result-oriented solution and lack details as to how the computer performed the modifications which is equivalent to the words apply it. Here there are no details about a particular trained machine learning or lemmatization algorithm other than it being used to determine information. Training is considered part of the abstract idea, as broadly recited in the claims as it is a mental process and certain method of organizing human activity to formulate rules based on previous data. The machine learning or lemmatization are used to generally apply the abstract idea without placing any limitation on how the machine learning or lemmatization operates to derive information. The limitation recites only the idea of using machine learning or lemmatization without details on how this is accomplished. The claim omits any details as to how the machine learning or lemmatization solves a technical problem and instead recites only the idea of a solution or outcome. The claim invokes a generic machine learning or lemmatization merely as a tool for making the calculation or determination rather than purporting to improve the technology or a computer. (additionally see for reference USPTO example 48).
Further, mental process and certain method of organizing human activity limitations that are instead recited as being performed at a high of generality by “an electric circuit” and “an electronic database”, the information is stored “a first, second, and third memory”, the text or content is “digital”, the rules are “machine learning”, and the linguistics is “lemmatization” merely results in generally linking the use of the judicial exception to the field of computers.
As per claim 19, the claims recite mental process and certain method of organizing human activities. Specifically it is a mental process and a certain method of organizing human activities to use the above process for hiring or education or more specifically as recited in the claims of determine target position and benchmark position matching for employment opportunities and education programs. This is part of the abstract idea. There are no additional elements beyond the those previously discussed above.
As per claim 20, the claims recite mental process and certain methods of organizing human activity. Specifically it is a mental process and a certain method of organizing human activities to receive competency or traits scores from a memory and statistically combine them. This is part of the abstract idea. The additional elements that the storage is a first memory and being performed by an electronic database, have been previously discussed above in claim 18, and results in the same analysis as above.
As per claim 21, the claims recite mental process and certain methods of organizing human activity. Specifically it is a mental process and a certain method of organizing human activity to provide a benchmark for comparison being a person who was offered the job. This is part of the abstract idea. There are no additional elements beyond the those previously discussed above.
As per claim 22, the claims recite mental process and certain methods of organizing human activities. Specifically it is mental process and certain methods of organizing human activities to have records related to responses to predetermined interview questions associated with a benchmark position and a target position where they both use the same interview questions and are analyzed under the same rules. This is part of the abstract idea. The additional elements that the content is digital and the rules are machine learning, have been previously discussed above in claim 18, and results in the same analysis as above.
As per claim 23, the claims recite mental process and certain methods of organizing human activities. Specifically it is a mental process and certain methods of organizing human activities to output a score or more specifically as recited in the claims to output a comparison with a quantitative representative of the benchmark score. This is part of the abstract idea. The additional elements that the information is instead performed by the “electronic circuit”, have been previously discussed above in claim 18, and results in the same analysis as above.
As per claim 24, the claims recite mental process and certain methods of organizing human activities. Specifically, it is a mental process and certain methods of organizing human activities to include categories and subcategories within benchmark and target positions or more specifically as recited in the claims of store benchmark and target positions to include position categories and position subcategories within the position categories. This is part of the abstract idea. The additional elements that the information is instead stored in a database, have been previously discussed above in claim 18, and results in the same analysis as above.
As per new claim 25, the claim recites the substantially similar limitations to those found in previously recited claim 18. Specifically, the claims recite the same method of organizing human activities and mental process limitations, therefore recite the same limitations that are part of the abstract idea. Accordingly the claims do not recite any further additional elements than those previously recited above in claim 18 that result in apply it or generally linking it to the field of computers, as discussed above.
The claim(s) does/do not include additional elements that are sufficient to amount to significantly more than the judicial exception because the claims merely recite limitations that are not indicative of an inventive concept (“significantly more”) in that the claims merely recite:
(1) Adding the words “apply it” ( or an equivalent) with the judicial exception, or mere instructions to implement an abstract idea on a computer, or merely uses a computer as a tool to perform an abstract idea (see MPEP 2106.05(f)), and (2) Generally linking the use of the judicial exception to a particular technological environment or field of use (see MPEP 2106.05(h)), as detailed above under the practical application step.
Claim Objections
13. Claim 18 is objected to because of the following informalities: Applicant recites “lemmantization”. This appears to be a typo and is interpreted as “lemmatization” as claimed in claim 25. Appropriate correction is required.
Claim Rejections - 35 USC § 103
14. In the event the determination of the status of the application as subject to AIA 35 U.S.C. 102 and 103 (or as subject to pre-AIA 35 U.S.C. 102 and 103) is incorrect, any correction of the statutory basis (i.e., changing from AIA to pre-AIA ) for the rejection will not be considered a new ground of rejection if the prior art relied upon, and the rationale supporting the rejection, would be the same under either status.
15. 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.
16. Claim(s) 18-25 are rejected under 35 U.S.C. 103 as being unpatentable over Ghosh et al. (United States Patent Application Publication Number: US 2021/0150486) further in view of Khandekar (United States Patent Application Publication Number: US 2014/0316768).
As per claim 18, Ghosh et al. teaches An electronic circuit comprising: (see paragraphs 0088-0090, Examiner’s note: teaches software running on a computer to perform the functions).
a first data memory holding multiple first sets of multiple digital text records, each different first set representing data from benchmark individuals of a given benchmark position; (see paragraphs 0027, 0033, and 0035, Examiner’s note: teaches multiple information for the benchmark person from databases or data stores).
A second data holding a second set of digital text records representing an evaluation individual for a target position; a third data holding a target position; (see paragraphs 0031-0032, 0044, 0046, 0049, 0050, Examiner’s note: comparing a job candidate to a job requirements (see paragraph 0046, 0044, and 0050). Teaches information may be text (see paragraphs 0049, 0031-0032)).
a preprocessor providing a lemmatization to the data of the first and; (see paragraphs 0031, Examiner’s note: lemmatization of information).
a set of machine learning systems communicating with the first and second data each machine learning system having a unique model trained to evaluate characteristics of text records to provide corresponding competencies and traits scores; (see paragraphs 0017, 0029, 0045-0047, 0050-0053, Examiner’s note: teaches how the computer modeling each input, to correlate with other information, to determine outputs like users for a job based on determined job requirements and benchmark users (e.g. wanting a user similar to user A)).
The system communicating with the first data memory to receive competency and trait scores indexed according to the benchmark positions and communicating with the third data to receive the target position and to output competency and trait scores according to an indexing by the target position; (see paragraphs 0044-0046, and 0064 Examiner’s note: teaches ranking job candidates)
wherein the electronic circuit operates sequentially on the first and second set of digital text records using a same models to output competency and trait scores from the second set of digital text records together with competency and trait scores from the first set of digital records indexed by the target position from the system to reduce the effects of bias in the models (see paragraphs 0047-0051, Examiner’s note: teaches comparing interview requests between a benchmark person and a potential candidate, to determine a job candidate, it is noted that “to reduce to effects of bias in the models” is intended use or intended result but does not require steps to be performed see MPEP 2111.04).
Wherein the traits are personality traits selected from the group consisting of: openness to experience, conscientiousness, extraversion, agreeableness, empathy, proactiveness, self-determination, and resilience; (see paragraph 0047-0049, Examiner’s note: Here Ghosh teaches determining a tone of a user’s response to an interview question or interview simulation to compare responses with another user who is the benchmark user (the user who the system is trying to find a similar user of) to determine a match. So here as these tones are related to simulations or interview questions in order to determine a match, given the different broad category label recitation in the claims of where a user is “fearful, frustrated, or angry” could read on agreeableness, openness to experience, resilience, or empathy, “analytical” could read on conscientiousness or proactiveness or self-determination, “excited” could read on extraversion or openness to experience, “polite” could be agreeableness, “sad” or “sympathetic” could read on empathy for each of the different corresponding questions asked or job simulations performed by the interviewee in the interview. There is no recitation in the claims as to how these different alternative members are calculated, specifically there is no distinction in the claims from the teachings above in Gosh of for example a user is polite during a question in an interview which means the user is has agreeableness and if the benchmark user was also polite about the interview question, the user can be a match).
Examiner notes: based on the above being a Markush claim, the Examiner interprets only one of the above alternatives required to be selected, as the claims require a selection from a closed group of alternative members. However, the Examiner has provided references to other members in the efforts of compact prosecution. See MPEP 2117, cited herein:
I. MARKUSH CLAIM
A "Markush" claim recites a list of alternatively useable members. In re Harnisch, 631 F.2d 716, 719-20, 206 USPQ 300, 302-304 (CCPA 1980); Ex parte Markush, 1925 Dec. Comm'r Pat. 126, 127 (1924). The listing of specified alternatives within a Markush claim is referred to as a Markush group or Markush grouping. Abbott Labs v. Baxter Pharmaceutical Products, Inc., 334 F.3d 1274, 1280-81, 67 USPQ2d 1191, 1196-97 (Fed. Cir. 2003) (citing to several sources that describe Markush groups). Claim language defined by a Markush grouping requires selection from a closed group "consisting of" the alternative members. Id. at 1280, 67 USPQ2d at 1196. See also Amgen Inc. v. Amneal Pharmaceuticals LLC, 945 F.3d 1368, 1376-78, 2020 USPQ2d 3197 (Fed. Cir. 2020) (stating that there is a strong presumption that a claim element set off with "consisting of" is closed to unrecited elements.). See MPEP § 2111.03, subsection II, for a discussion of the term "consisting of" in the context of Markush groupings.
Treatment of claims reciting alternatives is not governed by the particular format used (e.g., alternatives may be set forth as "a material selected from the group consisting of A, B, and C" or "wherein the material is A, B, or C"). See, e.g., the Supplementary Examination Guidelines for Determining Compliance with 35 U.S.C. 112 and for Treatment of Related Issues in Patent Applications ("Supplementary Guidelines"), 76 Fed. Reg. 7162 (February 9, 2011). Claims that set forth a list of alternatives from which a selection is to be made are typically referred to as Markush claims, after the appellant in Ex parte Markush, 1925 Dec. Comm’r Pat. 126, 127 (1924). Although the term "Markush claim" is used throughout the MPEP, any claim that recites alternatively usable members, regardless of format, should be treated as a Markush claim. Inventions in metallurgy, refractories, ceramics, chemistry, pharmacology and biology are most frequently claimed under the Markush formula, but purely mechanical features or process steps may also be claimed by using the Markush style of claiming. See, e.g., Fresenius USA, Inc. v. Baxter Int’l, Inc., 582 F.3d 1288, 1297-98, 92 USPQ2d 1163, 1170-1172 (Fed. Cir. 2009)(claim to a hemodialysis apparatus required "at least one unit selected from the group consisting of (i) a dialysate-preparation unit, (ii) a dialysate-circulation unit, (iii) an ultrafiltrate-removal unit, and (iv) a dialysate-monitoring unit" and a user/machine interface operably connected thereto); In re Harnisch, 631 F.2d 716, 206 USPQ 300 (CCPA 1980)(defining alternative moieties of a chemical compound with Markush groupings).
and wherein the competencies are selected from the group consisting of: feeling and deciding, supporting and cooperating, interacting and presenting, analyzing and interpreting, creating and conceptualizing, organizing and executing, adapting and coping, and enterprising and performing (see paragraph 0047-0049, Examiner’s note: Here Ghosh teaches determining a tone of a user’s response to an interview question or interview simulation to compare responses with another user asked who is the benchmark user (the user who the system is trying to find a similar user of) to determine a match. So here as these tones are related to simulations or interview questions in order to determine a match, given the different broad category label recitation in the claims of where a user is “excited” tone could read on adapting and coping, feeling and deciding, interacting and presenting, “satisfied” could read on supporting and cooperating, adapting and coping, or enterprising and performing, “impolite” could read on feeling and deciding, interacting and presenting, “frustrated” could read on interacting and presenting, feeling and deciding, analyzing and interpreting, creating and conceptualizing, organizing and executing, adapting and coping, and enterprising and performing, for each of the different corresponding questions asked or job simulations performed by the interviewee in the interview. This is no recitation in the claims as to how these different alternative members are calculated, specifically there is no distinction in the claims from the teachings above in Gosh of for example a user is analytical about a project simulation in an interview which means the user has an analyzing and interpreting competency and if the benchmark user was also analytical during that project simulation, the user can be a match).
Examiner notes: based on the above being a Markush claim, the Examiner interprets only one of the above alternatives required to be selected, as the claims require a selection from a closed group of alternative members. However, the Examiner has provided references to other members in the efforts of compact prosecution. See MPEP 2117, above).
Ghosh et al. does not expressly teach (1) storing information in different memories or more specifically as recited memories and including a second data memory and a third data memory, (2) performing lemmatization on job candidates or more specifically providing a lemmatization to second data memories, and (3) an electronic database performing operations
However, Khandekar which is in the art of natural language processing of text (see abstract) teaches (1) storing information in different memories or more specifically as recited memories and including a second data memory and a third data memory (see paragraph 0223, 0265, and Figure 6, Examiner’s note: storing processed information in different memories like a relational database, file, or XML to not have to reprocess the information again).
(2) performing lemmatization on job candidates or more specifically providing a lemmatization to second data memories, (see paragraphs 0175-0176, 0200-0201, 0219, Examiner’s note: matching resumes of candidates to specific job requirements (see paragraphs 0176-0176) and determining lemma (see paragraphs 0200-0201 and 0219).
And (3) an electronic database performing operations (see paragraph 0077-0078, Examiner’s note: database server to perform the functions).
Before the effective filing date of the claimed invention it would have been obvious for one of ordinary skill in the art to have modified Ghosh with the aforementioned teachings from Khandekar with the motivation of providing a way to have another type of common computer perform the functions (see Khandekar paragraph 0078), storing job information for later use in different memories to not have to reprocess information again (see Khandekar 0223, 0265, and Figure 6), as well as using lemmatization to perform analysis on other types of job placement information (see Khandekar paragraphs 0175-0176, 0200-0201, 0219), when performing the functions on a computer (see Ghosh paragraphs 0088-0090), using the job information to perform calculations (see Ghosh paragraphs 0042-0045), storing information in data stores for later use (see Ghosh paragraphs 0027, 0033, and 0035), and performing lemmatization on other information including job placement information (see Ghosh paragraphs 0031-0032) are all known.
As per claim 19, Ghosh teaches
wherein the target position and benchmark position matching the target position are selected from the group consisting of an employment opportunity and an education program (see paragraph 0015, Examiner’s note: employee of an organization, teaches an employment opportunity).
Examiner notes: based on the above being a Markush claim, the Examiner interprets only one of the above required by the claim as the claims require selection from a closed group of alternative members. See MPEP 2117, cited herein above in claim 1. Therefore Ghosh meets the requirements of the claim as they are listed in the alternative and Ghosh teaches the alternative of employment opportunity.
However, the Examiner has provided a reference teaching below to the other member of the group in the efforts of compact prosecution, as Khandekar (which is already cited in claim 18 from which claim 19 depends) teaches this other alternative member (not taught in Ghosh). Specifically Ghosh does not expressly teach and an educational program
However, Khandekar which is in the art of natural language processing of text (see abstract) teaches and an educational program (see paragraph 0175, Examiner’s note: matching patient candidates to clinical trials).
Before the effective filing date of the claimed invention it would have been obvious for one of ordinary skill in the art to have modified Ghosh in view of Khandekar with the aforementioned teachings from Khandekar with the motivation of providing a way to determine potential candidates for another type of project (see Khandekar paragraph 0017), when determining candidates for various types of projects or jobs is known (see Ghosh paragraphs 0015 and 0028).
As per claim 20, Ghosh teaches
wherein the system database further receives the competency and trait scores from the first data memory from multiple benchmark individuals and statistically combines them (see paragraphs 0017-0018, 0043, 0061-0063, Examiner’s note: teaches this could be for one or a team).
Ghosh et al. does not expressly teach an electronic database performing operations
However, Khandekar which is in the art of natural language processing of text (see abstract) teaches an electronic database performing operations (see paragraph 0077-0078, Examiner’s note: database server to perform the functions).
Before the effective filing date of the claimed invention it would have been obvious for one of ordinary skill in the art to have modified Ghosh in view of Khandekar with the aforementioned teachings from Khandekar with the motivation of providing a way to have another type of common computer perform the functions (see Khandekar paragraph 0078), when performing the functions on a computer (see Ghosh paragraphs 0088-0090) and databases as part of the system (see Ghosh paragraphs 0022-0023, and 0042) are all known.
As per claim 21, Ghosh teaches
wherein the benchmark individuals are individuals to whom a given benchmark position was offered (see paragraphs 0017-0018, 0026, and 0028, Examiner’s note: finding a person like our company’s chief engineer or who worked on a specific project).
As per claim 22, Ghosh teaches
wherein the digital text records include responses to a predetermined set of interview questions, and wherein the text records associated with each given corresponding benchmark position and target position use a same predetermined set of interview questions and the same set of machine learning systems (see paragraphs 0047-0052, Examiner’s note: teaches comparing interview responses for the same set between a potential candidate and the benchmark candidate).
As per claim 23, Ghosh teaches
wherein the electronic circuit outputs the comparison with a quantitative representation of the benchmark scores (see paragraphs 0044-0046, 0062, Examiner’s note: teaches ranking the potential candidates).
As per claim 24, Ghosh teaches
wherein the benchmark and target positions in the system include position categories and position subcategories arranged within the position categories (see paragraphs 0018, 0033-0035, Examiner’s note: teaches project X and information within project X (see paragraphs 0018 and 0035). Further teaches information related to a specific person or candidate attribute (see paragraphs 0033-0034)).
Ghosh et al. does not expressly teach an electronic database performing operations
However, Khandekar which is in the art of natural language processing of text (see abstract) teaches an electronic database performing operations (see paragraph 0077-0078, Examiner’s note: database server to perform the functions).
Before the effective filing date of the claimed invention it would have been obvious for one of ordinary skill in the art to have modified Ghosh in view of Khandekar with the aforementioned teachings from Khandekar with the motivation of providing a way to have another type of common computer perform the functions (see Khandekar paragraph 0078), when performing the functions on a computer (see Ghosh paragraphs 0088-0090) and databases as part of the system (see Ghosh paragraphs 0022-0023, and 0042) are all known.
As per claim 25, Ghosh et al. teaches a method of operating an electronic circuit, the method comprising: (see paragraphs 0088-0090 and 0095, Examiner’s note: teaches software running on a computer to perform the functions (see paragraphs 0088-0090). Further teaches program modules carry out methodologies (see paragraph 0095)).
Holding, in a first data memory of the electronic circuit, multiple first sets of digital text records, each different first set representing data from benchmark individuals of a given benchmark position; (see paragraphs 0027, 0033, and 0035, Examiner’s note: teaches multiple information for the benchmark person from databases or data stores).
Holding, in second data of the electronic circuit, a second set of digital text records representing an evaluation individual for a target position; holding, in a third data of the electronic circuit, data representing the target position; (see paragraphs 0031-0032, 0044, 0046, 0049, 0050, Examiner’s note: comparing a job candidate to job requirements (see paragraph 0046, 0044, and 0050). Teaches information may be text (see paragraphs 0049, 0031-0032)).
Providing, by a preprocessor of the electronic circuit, providing the digital text records of the first data memories by performing lemmatization; (see paragraphs 0031, Examiner’s note: lemmatization of information).
Communicating, by a set of machine learning systems of the electronic circuit, with the first and second data each machine learning system having a unique model trained to evaluate characteristics of text records to provide corresponding competencies and traits scores; (see paragraphs 0017, 0029, 0045-0047, 0050-0053, Examiner’s note: teaches how the computer modeling each input, to correlate with other information, to determine outputs like users for a job based on determined job requirements and benchmark users (e.g. wanting a user similar to user A)).
Communicating, by the system of the electronic circuit, with the first data memory to receive competency and trait scores indexed according to the benchmark positions and communicating with the third data to receive the target position and to output competency and trait scores according to an indexing by the target position; (see paragraphs 0044-0046, and 0064 Examiner’s note: teaches ranking job candidates)
wherein the electronic circuit operates sequentially on the first and second set of digital text records using the same models to output competency and trade scores from the second set of digital text records together with competency and trait scores from the first set of digital records indexed by the target position from the system to reduce the effects of bias in the models (see paragraphs 0047-0051, Examiner’s note: teaches comparing interview requests between a benchmark person and a potential candidate, to determine a job candidate, it is noted that “to reduce to effects of bias in the models” is intended use or intended result but does not require steps to be performed see MPEP 2111.04).
Ghosh et al. does not expressly teach (1) storing information in different memories or more specifically as recited memories and including a second data memory and a third data memory, (2) performing lemmatization on job candidates or more specifically providing second data memories by performing lemmatization, and (3) an electronic database performing operations
However, Khandekar which is in the art of natural language processing of text (see abstract) teaches (1) storing information in different memories or more specifically as recited memories and including a second data memory and a third data memory (see paragraph 0223, 0265, and Figure 6, Examiner’s note: storing processed information in different memories like a relational database, file, or XML to not have to reprocess the information again).
(2) performing lemmatization on job candidates or more specifically providing second data memories by performing lemmatization, (see paragraphs 0175-0176, 0200-0201, 0219, Examiner’s note: matching resumes of candidates to specific job requirements (see paragraphs 0176-0176) and determining lemma (see paragraphs 0200-0201 and 0219).
And (3) an electronic database performing operations (see paragraph 0077-0078, Examiner’s note: database server to perform the functions).
Before the effective filing date of the claimed invention it would have been obvious for one of ordinary skill in the art to have modified Ghosh with the aforementioned teachings from Khandekar with the motivation of providing a way to have another type of common computer perform the functions (see Khandekar paragraph 0078), storing job information for later use in different memories to not have to reprocess information again (see Khandekar 0223, 0265, and Figure 6), as well as using lemmatization to perform analysis on other types of job placement information (see Khandekar paragraphs 0175-0176, 0200-0201, 0219), when performing the functions on a computer (see Ghosh paragraphs 0088-0090), using the job information to perform calculations (see Ghosh paragraphs 0042-0045), storing information in data stores for later use (see Ghosh paragraphs 0027, 0033, and 0035), and performing lemmatization on other information including job placement information (see Ghosh paragraphs 0031-0032) are all known.
Conclusion
17. The prior art made of record and not relied upon is considered pertinent to applicant's disclosure:
Baldwin et al. (United States Patent Number: US 7,502,748) teaches a system for matching employment candidates with employment positions based on personality traits and additional teaches using information from users currently filling positions (benchmark users) to determine suitable employees for each position (see abstract) and further teaches traits include things like conscientiousness, organization, extraversion, group orientation, stability, poise, social desirability (see Figure 3).
Grover et al. (United States Patent Application Publication Number: US 2018/0173802) teaches determining similarities among industries to enhance job searching where this is determined through lemmatization (see abstract and paragraphs 0074-0075 ad 0143)
18. Any inquiry concerning this communication or earlier communications from the examiner should be directed to KIERSTEN SUMMERS whose telephone number is (571)272-6542. The examiner can normally be reached Monday - Friday 7am-3:30pm.
Examiner interviews are available via telephone, in-person, and video conferencing using a USPTO supplied web-based collaboration tool. To schedule an interview, applicant is encouraged to use the USPTO Automated Interview Request (AIR) at http://www.uspto.gov/interviewpractice.
If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, Nathan Uber can be reached on 5712703923. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
Information regarding the status of published or unpublished applications may be obtained from Patent Center. Unpublished application information in Patent Center is available to registered users.
To file and manage patent submissions in Patent Center, visit: https://patentcenter.uspto.gov. Visit https://www.uspto.gov/patents/apply/patent-center for more information about Patent Center and https://www.uspto.gov/patents/docx for information about filing in DOCX format.
For additional questions, contact the Electronic Business Center (EBC) at 866-217-9197 (toll-free). If you would like assistance from a USPTO Customer Service Representative, call 800-786-9199 (IN USA OR CANADA) or 571-272-1000.
/KIERSTEN V SUMMERS/Primary Examiner, Art Unit 3626
