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 .
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and the fee set forth in 37 CFR 1.17(e) has been timely paid, the finality of the previous Office action has been withdrawn pursuant to 37 CFR 1.114.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 01/21/2026 has been entered.
Claims 1-6, 8-17, 19, 20 are pending and have been examined on the merits set forth below.
Response to Arguments
Applicant's arguments filed with respect to rejections under 35 USC 101 have been fully considered but they are not persuasive. Applicant alleges claims are eligible based on amendments without any further discussion. Examiner disagrees and has updated the rejection below.
Examiner acknowledges the cancellation of claim 18. The rejection under 35 USC 112(d) has been withdrawn.
Applicant’s arguments with respect to claim(s) as newly rejected have been considered but are moot because the new ground of rejection does not rely on any reference applied in the prior rejection of record for any teaching or matter specifically challenged in the argument. Smith et al has been added to the rejection under 35 USC 103.
Claim Rejections - 35 USC § 101
35 U.S.C. 101 reads as follows:
Whoever invents or discovers any new and useful process, machine, manufacture, or composition of matter, or any new and useful improvement thereof, may obtain a patent therefor, subject to the conditions and requirements of this title.
Claim(s) 1-6, 8-17, 19, 20 are rejected under 35 U.S.C. 101 because the claimed invention is directed to a judicial exception (i.e., a law of nature, a natural phenomenon, or an abstract idea) without significantly more. Claim(s) 1-6, 8-17, 19, 20 is/are directed to a method, system, and computer program product. Thus, all the claims are within the four potentially eligible categories of invention (a process, a machine and an article of manufacture, respectively), satisfying Step 1 of the Subject Matter Eligibility (SME) test.
As per Prong One of Step 2A of the §101 eligibility analysis set forth in MPEP 2106, the Examiner notes that the claims recite mental processes. More specifically, the independent claims 1, 12 and 20 recite:
identifying, sources associated with a respective sub-system of a platrform (Mental Process – observation/evaluation performed in the mind or with pen/paper);
providing,
obtaining,
providing,
obtaining,
updating a database associated with the organization to indicate whether the respective benefaction rating for the at least one respective worker of the plurality of workers satisfies one or more criteria associated with a goal associated with the organization (Mental Process – observation/evaluation performed in the mind or with pen/paper); and
removing the data associated with the actions performed by the plurality of workers from each respective data source of the plurality of data sources across the respective sub-systems of the platform (Mental Process – observation/evaluation performed in the mind or with pen/paper).
As indicated next to each limitation, the claim recites mental processes. Specifically, evaluations and observations that may be performed in the mind or with pen and paper are recited. In addition, the concept of determining a benefaction level for workers based on machine learning and normalization wherein the benefaction rating reflects the benefaction level of a worker relative to other workers and maintaining a database of such evaluations is Certain Method of Organizing Human Activity as it relates to managing personal behavior of people and/or business relations.
The nominal recitation of a platform and machine learning models in claim 1, a system comprising a memory and a processing device coupled to the memory in claim 12, and a non-transitory computer readable medium comprising instructions that are executed by a processing device in claim 20, does not necessarily preclude the claim from reciting an abstract idea as evidenced by the analysis at Prong 2 of Step 2A.
Regarding Prong Two of Step 2A, a claim reciting an abstract idea must be analyzed to determine whether any additional elements in the claim integrate the judicial exception into a practical application. Limitations that are indicative of integration into a practical application include: Improvements to the functioning of a computer, or to any other technology or technical field, as discussed in MPEP 2106.05(a); Applying or using a judicial exception to effect a particular treatment or prophylaxis for disease or medical condition – see Vanda Memo; Applying the judicial exception with, or by use of, a particular machine, as discussed in MPEP 2106.05(b); Effecting a transformation or reduction of a particular article to a different state or thing, as discussed in MPEP 2106.05(c); and Applying or using the judicial exception in some other meaningful way beyond generally linking the use of the judicial exception to a particular technological environment, such that the claim as a whole is more than a drafting effort designed to monopolize the exception, as discussed in MPEP 2106.05(e) and the Vanda Memo issued in June 2018.
In this case, the independent claims do not include limitations that meet the criteria listed above, thus the abstract idea is not integrated into a practical application. A platform and machine learning models in claim 1, a system comprising a memory and a processing device coupled to the memory in claim 12, and a non-transitory computer readable medium comprising instructions that are executed by a processing device in claim 20 amount to using a computer as a tool to perform the abstract idea.
The dependent claims further limit the abstract idea and some recite additional elements that do not integrate the abstract idea into a practical application.
Claims 2-4, 13-15 recite steps of updating a database of digital badge information which is a mental process that can practically be performed with pen and paper The use of a database and profile hosted by an application amounts to using a computer as a tool to perform the abstract idea and does not integrate the abstract idea into a practical application.
Claims 5-6, 16-17 recite transmitting data to a blockchain which amounts to using a computer as a tool to store information and does not integrate the abstract idea into a practical application.
Claim 8, 19 recites updating a benefaction level and comparing to a criteria which is a mental process that ca practically be performed in the mind or with pen and paper. Providing data to a machine learning model amounts to using a computer as a tool to perform the analysis. There is no integration into a practical application.
Claim 9 recites receiving data associated with a goal which is an abstract mental process. Receiving data from a client device amounts to using a computer as a tool to perform the abstract idea and does not integrate the abstract idea into a practical application.
Claim 10 specifies the machine learning model is a neural network which amounts to using a computer as a tool to perform the abstract idea and there is no integration into a practical application.
Claim 11 recites details about training the machine learning model which amounts to using a computer as a tool to perform an abstract idea. There is no integration into a practical application.
The claims do not include limitations beyond generally linking the use of the abstract idea to a particular technological environment. When considered individually, the system and software claim elements only contribute generic recitations of technical elements to the claims. It is readily apparent, for example, that the claim is not directed to any specific improvements of these elements. The invention is not directed to a technical improvement. When the claims are considered individually and as a whole, the additional elements noted above appear to merely apply the abstract concept to a technical environment in a very general sense.
Lastly and in accordance with Step 2B, the claim does not include additional elements that are sufficient to amount to significantly more than the judicial exception. As discussed above with respect to integration of the abstract idea into a practical application, the additional elements amount to no more than mere instruction to apply the exception using generic computer component. Mere instruction to apply an exception using generic computer components cannot provide an inventive concept.
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) 1-6 and 8-17, 19, 20 is/are rejected under 35 U.S.C. 103 as being unpatentable over Rankins, US 2024/0330822, in view of El Kharzazi, US 2019/0180244, George et al, US 2003/0158818, and Smith et al, US 2011/0276356.
As per claim 1, 12 and 20 Rankins discloses a method, a system comprising: a memory; and a processing device coupled to the memory, the processing device to perform operations [abstract] and non-transitory computer readable medium comprising instructions [0052] that, when executed by a processing device, cause the processing device to perform operations comprising: identifying, by a platform, data associated with actions performed by a plurality of workers of an organization, wherein the data is identified from a plurality of data sources each associated with a respective subsystem of the platform ([0133-0140] – performance management module tracks worker action data related to goals; [0017] - performance management module is described as managing, tracking, aligning and rewarding participants by tracking and retrieving performance metrics across various modules in one integrated platform);
providing, by the platform, based on given input data indicating actions of workers, a benefaction level associated with the workers relative to other workers of the organization ([0144-0145] – development levels are generated and displayed so a team member can understand their present position and progress);
obtaining, by the platform, the one or more outputs indicate a respective benefaction level for each of the plurality of workers relative to the other workers of the plurality of workers ([0141, 0144-0145] – development levels are generated and displayed so a team member can understand their present position and progress);
updating a database associated with the organization to indicate whether the respective benefaction level for the respective worker of the plurality of workers satisfies the one or more criteria associated with the goal defined by at least one of the respective worker or the organization ([0144-0145] – the system updates awards and leaderboards as a results of team member performance).
Rankins fails to explicitly disclose the identified data as input to a machine learning model, wherein the machine learning model is trained to predict, [the benefaction level]. El Kharzazi describes the use of machine learning, neural network and blockchain methodologies for Human Resource Management to evaluate employee performance, impact, etc. [abstract, 0023+]. It would have been obvious to one of ordinary skill in the art at the time of the invention to include in the system of Rankins the ability to utilize machine learning as taught by El Kharzazi since the claimed invention is merely a combination of old elements and in the combination each element merely would have performed the same function as it did separately, and one of ordinary skill in the art would have recognized that the results of the combination were predictable.
Rankins as modified by El Kharzazi discloses maintaining the performance data across respective sub-systems of the platform as described above, but fails to explicitly disclose removing the data associated with the actions performed by the plurality of workers from each respective data source of the plurality of data sources across the respective sub-systems of the platform. George et al discloses a system that tracks point totals for individuals across various award systems and resets upon issuance of a reward. It would have been obvious to one of ordinary skill in the art at the time of the invention to include in the system of Rankins the ability to remove data after a point amount is reached for award as taught by George et al since the claimed invention is merely a combination of old elements and in the combination each element merely would have performed the same function as it did separately, and one of ordinary skill in the art would have recognized that the results of the combination were predictable.
While Rankins describes at [0141, 0144-0145] – development levels are generated and displayed so a team member can understand their present position and progress], the combination of references fails to explicitly disclose providing, the indicated respective benefaction level for each of the plurality of workers as an input to a normalization function; and obtaining based on one or more outputs of the normalization function, a benefaction rating for each respective worker of the plurality of workers, wherein the benefaction rating for a respective worker reflects the benefaction level of the respective worker relative to the benefaction level of other workers of the plurality of workers. Smith et al in an analogous worker assessment system discloses determining a performance level assessment, normalizing the assessment data, comparing the data and ranking to determine a rank for each worker based on the comparison of the performance data. [0050-0052]. It would have been obvious to one of ordinary skill in the art at the time of the invention to include in the system of Rankins the ability to normalize the ratings as taught by Smith et al since the claimed invention is merely a combination of old elements and in the combination each element merely would have performed the same function as it did separately, and one of ordinary skill in the art would have recognized that the results of the combination were predictable. Further data normalization standardizes information making it easier to manage and analyze.
As per claim 2 and 13, Rankins discloses the method of claim 1, wherein updating the database to indicate whether the respective benefaction rating for the at least one respective worker of the plurality of workers satisfies the one or more criteria associated with the goal comprises: updating the database to include at least one of an indication that a digital badge is issued to the at least one respective worker or an indication that a digital badge is not issued to the at least one respective worker, wherein the digital badge indicates that the respective benefaction level for the at least one respective worker satisfies the one or more criteria associated with the goal ([0017, 0106, 0141] – tracking goals of individuals and awarding badges for successful goal delivery and update of leaderboards to display best performers and metrics).
As per claim 3 and 14, Rankins discloses the method of claim 2, further comprising: identifying a profile associated with the at least one respective worker, wherein the profile is hosted by an application, and wherein users of the application can access information associated with the at least one respective worker via the profile ([0055, 0056, 0106] – badges and achievements stored to user profile);
identifying an entry of the database that is associated with the at least one respective worker; extracting, from the identified entry of the database, one or more digital badges issued to the at least one respective worker, the one or more digital badges comprising the digital badge ([0132-0141] – performance management module maintains and tracks goals and badges awarded); and
updating the profile associated with the at least one respective worker to include an indication of each of the extracted one or more digital badges ([0132-0141] – performance management module maintains and tracks goals and badges awarded).
As per claim 4 and 15, Rankins discloses the method of claim 3, wherein the application hosting the profile is associated with at least one of the organization or a social media platform ([0055] – computer running an application that allows participants to be registered members and are provided with individual/team profile).
As per claim 5 and 16, Rankins discloses the method of claim 1, wherein the database is a distributed ledger network, and wherein updating the database comprises: transmitting an indication of whether the respective benefaction rating for the at least one respective worker of the plurality of workers satisfies the one or more criteria associated with the goal to one or more nodes of the distributed ledger network ([0059, 0063] – data repository may be local storage, cloud-based storage or blockchain storage).
As per claim 6 and 17, Rankins discloses the method of claim 5, wherein the distributed ledger network is a blockchain network ([0059, 0063] – data repository may be local storage, cloud-based storage or blockchain storage).
As per claim 8 and 19, Rankins discloses the method of claim 1, further comprising: responsive to determining that the respective benefaction rating for the at least one respective worker does not satisfy the one or more criteria, providing additional data indicating an action of the at least one respective worker; obtaining one or more additional outputs, wherein the one or more additional outputs indicate an updated benefaction rating for the at least one respective worker relative to the other workers; and determining whether the updated benefaction rating for the at least one respective worker satisfies the one or more criteria ([0144] – as each worker strives to achieve their goals the system tracks levels of development based on badges earned relative to others in the team or organization.
Rankins fails to explicitly disclose the identified data as input to a machine learning model, wherein the machine learning model is trained to predict, [the benefaction level]. El Kharzazi describes the use of machine learning, neural network and blockchain methodologies for Human Resource Management to evaluate employee performance, impact, etc. [abstract, 0023+]. It would have been obvious to one of ordinary skill in the art at the time of the invention to include in the system of Rankins the ability to utilize machine learning as taught by El Kharzazi since the claimed invention is merely a combination of old elements and in the combination each element merely would have performed the same function as it did separately, and one of ordinary skill in the art would have recognized that the results of the combination were predictable.
As per claim 9, Rankins discloses the method of claim 1, further comprising: receiving, from a client device associated with at least one of a representative of the organization or the at least one respective worker, an indication of the goal and the one or more criteria associated with the goal ([0145] – the system tracks associated metrics for each goal and indications of when goals are met).
As per claim 10, Raskins fails to explicitly disclose, while El Kharzazi describes the use of machine learning, neural network and blockchain methodologies for Human Resource Management to evaluate employee performance, impact, etc. [abstract, 0023+]. It would have been obvious to one of ordinary skill in the art at the time of the invention to include in the system of Rankins the ability to utilize machine learning as taught by El Kharzazi since the claimed invention is merely a combination of old elements and in the combination each element merely would have performed the same function as it did separately, and one of ordinary skill in the art would have recognized that the results of the combination were predictable.
As per claim 11, Raskins fails to disclose, while El Kharzazi discloses the method of claim 1, wherein the machine learning model is trained based on a data set comprising historical data associated with at least one of historical actions or historical activities by workers of the organization, criteria of a historical goal defined by the organization or one or more workers of the organization, and a historical benefaction level determined for the at least one of the historical actions or the historical activities by the one or more workers based on the criteria of the historical goal ([0023-0026] – neural networks are trained based on employee data). It would have been obvious to one of ordinary skill in the art at the time of the invention to include in the system of Rankins the ability to utilize machine learning as taught by El Kharzazi since the claimed invention is merely a combination of old elements and in the combination each element merely would have performed the same function as it did separately, and one of ordinary skill in the art would have recognized that the results of the combination were predictable.
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure. Pertinent art is listed in the attached PTO-892.
Any inquiry concerning this communication or earlier communications from the examiner should be directed to JOHNNA LOFTIS whose telephone number is (571)272-6736. The examiner can normally be reached M-F 7:00am-3:30pm.
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If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, Brian Epstein can be reached at 571-270-5389. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
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JOHNNA LOFTIS
Primary Examiner
Art Unit 3625
/JOHNNA R LOFTIS/Primary Examiner, Art Unit 3625