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 Final Office Action in response to communication received on 9/29/2025. Claims 18-25 are pending in this office action.
Response to Amendment
Applicant’s cancellation of claims 1-17 is acknowledged. Applicant’s amendments to claims 18 and 23 are acknowledged. Applicant’s addition of new claim 25 is acknowledged.
Response to Arguments
On Remarks page 11, Applicant argues that the claims as a whole do not recite a mental process as if the claims were characterized as such it would fail to acknowledge the claim’s hardware limitations.
The Examiner respectfully disagrees. The “hardware” limitations have been considered and then addressed under the practical application and significantly more step as detailed in the 101 rejection below. Further, mental processes and certain methods of organizing human activity can recite a computer, and still be found to be an abstract idea.
See MPEP 2106.04(a)(2) (cited herein):
Nor do the courts distinguish between claims that recite mental processes performed by humans and claims that recite mental processes performed on a computer. As the Federal Circuit has explained, "[c]ourts have examined claims that required the use of a computer and still found that the underlying, patent-ineligible invention could be performed via pen and paper or in a person’s mind." Versata Dev. Group v. SAP Am., Inc., 793 F.3d 1306, 1335, 115 USPQ2d 1681, 1702 (Fed. Cir. 2015). See also Intellectual Ventures I LLC v. Symantec Corp., 838 F.3d 1307, 1318, 120 USPQ2d 1353, 1360 (Fed. Cir. 2016) (‘‘[W]ith 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.’’); Mortgage Grader, Inc. v. First Choice Loan Servs. Inc., 811 F.3d 1314, 1324, 117 USPQ2d 1693, 1699 (Fed. Cir. 2016) (holding that computer-implemented method for "anonymous loan shopping" was an abstract idea because it could be "performed by humans without a computer"). Mental processes recited in claims that require computers are explained further below with respect to point C.
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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.
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Commercial interactions" or "legal interactions" include agreements in the form of contracts, legal obligations, advertising, marketing or sales activities or behaviors, and business relations.
An example of a claim reciting a commercial or legal interaction, where the interaction is an agreement in the form of contracts, is found in buySAFE, Inc. v. Google, Inc., 765 F.3d. 1350, 112 USPQ2d 1093 (Fed. Cir. 2014). The agreement at issue in buySAFE was a transaction performance guaranty, which is a contractual relationship. 765 F.3d at 1355, 112 USPQ2d at 1096. The patentee claimed a method in which a computer operated by the provider of a safe transaction service receives a request for a performance guarantee for an online commercial transaction, the computer processes the request by underwriting the requesting party in order to provide the transaction guarantee service, and the computer offers, via a computer network, a transaction guaranty that binds to the transaction upon the closing of the transaction. 765 F.3d at 1351-52, 112 USPQ2d at 1094. The Federal Circuit described the claims as directed to an abstract idea because they were "squarely about creating a contractual relationship--a ‘transaction performance guaranty’." 765 F.3d at 1355, 112 USPQ2d at 1096.
Therefore the Examiner respectfully disagrees.
On Remarks pages 13-14, Applicant argues that the claims as a whole does not recite a commercial or legal interaction. Specifically the claims recite 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. This is a commercial or legal interaction. Arguments towards improved outputs are addressed under the practical application and or significantly more steps.
On Remarks page 19, 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, 1743-44 (Fed. Cir. 2016); Intellectual Ventures I v. 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 I v. 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 1743-44 (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 Indem. 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 I v. 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 DDR 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. DDR 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.
Therefore based on the above, the Examiner respectfully disagrees.
On Remarks pages 19-20, Applicant argues an improvement to prior art systems and argues paragraphs 0074 and 0076 of the specification as filed. Here Applicant merely discusses considering different people for a potential job which is part of the abstract idea and not an improvement to the circuit.
On Remarks pages 20-21, Applicant argues improving by reducing bias. This is not discussed as an improvement in the specification nor has Applicant cited to where such an improvement with respect to bias is found in the specification. Accordingly in view of MPEP 2106.05(a), cited herein in this response to arguments section above, the Examiner does not find the argument persuasive.
On Remarks pages 21-23, Applicant argues inventive concept. Here Applicant again argues improving by reducing bias. This is not discussed as an improvement in the specification nor has Applicant cited to where such an improvement with respect to bias is found in the specification. Accordingly in view of MPEP 2106.05(a), the Examiner does not find the argument persuasive.
Applicant arguments with respect to the 112 second/b rejections are acknowledged. The previous lack of antecedent rejection with respect to “effects” has not been argued or amended so that the rejection remains. Further new claim 25 recites the previous “the same models” (similar claim language to previous claim 18) so a lack of antecedent basis rejection has been made.
With respect to the prior art, Applicant argues on pages 24-25 of Remarks , “Beyond those points, Applicant asserts that Ghosh does not disclose an electronic circuit that is sequentially operating with the same models.” This is a very broad limitation in the claim and is at least taught by Ghosh at the cited paragraphs of 0046-0051 in multiple ways. Here paragraphs 0046-0049 teaches comparing a candidate with former or current employes who engaged in a same or similar interview process or similar job by for example a tone and emotion analyzers (see paragraphs 0046-0049). Additionally the system can generate a questionnaire to present to a job candidate that mirrors those previously presented to Ms. Smith or Mr. Jones( desired candidate) (see paragraphs 0050-0051). The tone and emotion analyzers and or the questionnaires can be considered “a set of same models” and this is done sequentially as first this is performed on the desired user and then on the potential candidate.
Therefore the Examiner respectfully disagrees.
Claim Rejections - 35 USC § 101
35 U.S.C. 101 reads as follows:
Whoever invents or discovers any new and useful process, machine, manufacture, or composition of matter, or any new and useful improvement thereof, may obtain a patent therefor, subject to the conditions and requirements of this title.
Claims 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 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 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 the effects of bias in the models.
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 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 electronic database to reduce the effects of bias in the models.
As per claim 18, the claims recite limitations a human or humans could reasonably and practically perform. Specifically a human could 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, 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 users may choose to sequentially operate or review information on different sets of data with the same rules. The additional elements that these limitations that could be performed by a human or humans are instead 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 humans can 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 USPTO example 48).
Further, limitations that could be performed by a human or humans 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 per claim 19, the claims recite limitations a human or humans could reasonably and practically perform. Specifically a human could determine target position and benchmark posting matching for employment opportunities and education programs. There are no additional elements beyond the those previously discussed above.
As per claim 20, the claims recite limitations a human or humans could reasonably and practically perform. Specifically a human could receive competency or traits scores from a memory and statistically combine them. 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 limitations a human or humans could reasonably and practically perform. Specifically a human could provide a benchmark for comparison being a person who was offered the job. There are no additional elements beyond the those previously discussed above.
As per claim 22, the claims recite limitations a human or humans could reasonably and practically perform. Specifically a human can 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. 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 limitations a human or humans could reasonably and practically perform. Specifically a human could output a comparison with a quantitative representative of the benchmark score. 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 limitations a human or humans could reasonably and practically perform. Specifically a human could store benchmark and target positions to include position categories and position subcategories within the position categories. 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. Accordingly the claims do not recite any further additional elements then 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 Rejections - 35 USC § 112
The following is a quotation of 35 U.S.C. 112(b):
(b) CONCLUSION.—The specification shall conclude with one or more claims particularly pointing out and distinctly claiming the subject matter which the inventor or a joint inventor regards as the invention.
The following is a quotation of 35 U.S.C. 112 (pre-AIA ), second paragraph:
The specification shall conclude with one or more claims particularly pointing out and distinctly claiming the subject matter which the applicant regards as his invention.
Claims 18-25 is rejected under 35 U.S.C. 112(b) or 35 U.S.C. 112 (pre-AIA ), second paragraph, as being indefinite for failing to particularly point out and distinctly claim the subject matter which the inventor or a joint inventor (or for applications subject to pre-AIA 35 U.S.C. 112, the applicant), regards as the invention.
As per claim 18, Applicant recites the following limitation in the claim
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 trade scores from the second set of digital text records together with competency and trait scores form the first set of digital records indexed by the target position from the electronic database to
reduce the effects of bias in the models. There is insufficient antecedent basis for the limitations, the effects of bias, in the claim as the limitations are not previously recited in the claim. For the purposes of this examination, the Examiner will interpret the claim as follows: 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 trade scores from the second set of digital text records together with competency and trait scores form the first set of digital records indexed by the target position from the electronic database to reduce .
Further claims 19-24 that depend off claim 18 are rejected based on their
dependency under 35 U.S.C. 112(b) or 35 U.S.C. 112 (pre-AIA ), second paragraph, as
being indefinite for failing to particularly point out and distinctly claim the subject matter
which the inventor or a joint inventor (or for applications subject to pre-AIA 35 U.S.C.
112, the applicant), regards as the invention.
As per new claim 25, Applicant recites the following limitation in the claim wherein, by the electronic circuit, sequentially operating 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 form the first set of digital records indexed by the target position from the electronic database to reduce the effects of bias in the models. There is insufficient antecedent basis for the limitations, the same models and the effects of bias, in the claim as the limitations are not previously recited in the claim. For the purposes of this examination, the Examiner will interpret the claim as follows: 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 trade scores from the second set of digital text records together with competency and trait scores form the first set of digital records indexed by the target position from the electronic database to reduce .
Claim Rejections - 35 USC § 103
In the event the determination of the status of the application as subject to AIA 35 U.S.C. 102 and 103 (or as subject to pre-AIA 35 U.S.C. 102 and 103) is incorrect, any correction of the statutory basis (i.e., changing from AIA to pre-AIA ) for the rejection will not be considered a new ground of rejection if the prior art relied upon, and the rationale supporting the rejection, would be the same under either status.
The following is a quotation of 35 U.S.C. 103 which forms the basis for all obviousness rejections set forth in this Office action:
A patent for a claimed invention may not be obtained, notwithstanding that the claimed invention is not identically disclosed as set forth in section 102, if the differences between the claimed invention and the prior art are such that the claimed invention as a whole would have been obvious before the effective filing date of the claimed invention to a person having ordinary skill in the art to which the claimed invention pertains. Patentability shall not be negated by the manner in which the invention was made.
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 lemmantization 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 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).
Ghosh et al. does not expressly teach (1) 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) 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 (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), and performing lemmatization on 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 (see paragraph 0015, Examiner’s note: employee of an organization).
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 a 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).
Ghosh et al. does not expressly teach (1) 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) 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 (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), and performing lemmatization on job placement information (see Ghosh paragraphs 0031-0032) are all known.
Conclusion
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 nonprovisional extension fee (37 CFR 1.17(a)) 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 mailing date of this final action.
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure:
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)
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/KIERSTEN V SUMMERS/Primary Examiner, Art Unit 3626
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