DETAILED ACTION
This is a non-final, first office action on the merits. Claims 1-18 are pending. The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA .
Claim Objections
Regarding claim 15 is objected to because of the following informalities:
Claim 1 recites iii. Processing said employee performance data using said machine learning algorithms from said artificial intelligence system to generate a employee performance evaluation. It should recite generate an employee performance evaluation.
Claim 4 requires a period instead of semicolon at the end of the last limitation.
Claim 10 recites A employee data input module configured to receive employee-related performance data from multiple sources, said data including performance metrics and associated metadata. It should recite an employee data input module configured to………...
Appropriate correction is required.
Claim Rejections - 35 USC § 101
35 U.S.C. 101 reads as follows:
Whoever invents or discovers any new and useful process, machine, manufacture, or composition of matter, or any new and useful improvement thereof, may obtain a patent therefor, subject to the conditions and requirements of this title.
Claims 1-9 and 18 are rejected under 35 U.S.C. 101 because the claimed invention is directed to non-statutory subject matter. Specifically, claims 1-9 and 18 are directed to an abstract idea without additional elements amounting to significantly more than the abstract idea.
With respect to Step 2A Prong One of the framework, claims 1 and 18 recite an abstract idea. Claims 1 and 18 include “ receiving real-time work goals and assignments for one or more employees; calculating employee performance data based on the completion of said real-time work goals and assignments; processing said employee performance data to generate a employee performance evaluation; and storing said employee performance evaluation, Wherein said generated performance evaluations are usable for predicting/improving employee performance; collecting employee performance data from one or more data sources and storing said employee performance data; storing said employee performance data; providing for analysis of said employee performance data; providing a performance evaluator to analyze said employee performance data; providing at least one or more predictions based upon analysis of said employee performance data; and using said one or more predictions to execute instructions for employee mitigation”.
The limitations above recite an abstract idea under Step 2A Prong One. More particularly, the elements above recite mental processes-concepts performed in the human mind (including an observation, evaluation, judgment, opinion) and mathematical calculations because the elements describe a process for evaluating employee performance. As a result, claims 1 and 18 recite an abstract idea under Step 2A Prong One.
Claims 2-9 further describe the process for evaluating employee performance. As a result, claims 2-9 recite an abstract idea under Step 2A Prong One for the same reasons as stated above with respect to claims 1 and 18.
With respect to Step 2A Prong Two of the framework, claims 1 and 18 do not include additional elements that integrate the abstract idea into a practical application. Claims 1 and 18 include additional elements that do not recite an abstract idea under Step 2A Prong One. The additional elements of claims 1 and 18 include processors, a non-transitory computer readable storage medium, a blockchain system, server, an artificial intelligence system, machine learning algorithms, and a real-time. When considered in view of the claim as a whole, the additional elements do not integrate the abstract idea into a practical application because the additional computing elements are generic computing elements that are merely used as a tool to perform the recited abstract idea. As a result, claims 1 and 18 do not include additional elements that integrate the abstract idea into a practical application under Step 2A Prong Two.
Claims 4-7 do not include any additional elements beyond those recited with respect to claims 1 and 18. As a result, claims 4-7 do not include additional elements that integrate the abstract idea into a practical application under Step 2A Prong Two for the same reasons as stated above with respect to claims 1 and 18.
Claims 2-3 and 8-9 include additional elements that do not recite an abstract idea under Step 2A Prong One. The additional elements of claims 2-3 and 8-9 include additional elements that do not recite an abstract idea under Step 2A Prong One. The additional elements of claims 2-3 and 8-9 include an artificial intelligence system, artificial intelligence engines, blockchain systems, a server, a communication network, electronic blocks, and a server. When considered in view of the claims as a whole, the additional elements do not integrate the abstract idea into a practical application because the additional computing elements do no more than generally link the use of the recited abstract idea to a particular technological environment. As a result, claims 2-3 and 8-9 do not include additional elements that integrate the abstract idea into a practical application under Step 2A Prong Two.
With respect to Step 2B of the framework, claims 1 and 18 do not include additional elements amounting to significantly more than the abstract idea. As noted above, claims 1 and 18 include additional elements that do not recite an abstract idea under Step 2A Prong One. The additional elements of claims 1 and 18 include processors, a non-transitory computer readable storage medium, a blockchain system, server, an artificial intelligence system, machine learning algorithms, and a real-time. The additional elements do not amount to significantly more than the abstract idea because the additional computing elements are generic computing elements that are merely used as a tool to perform the recited abstract idea. Further, looking at the additional elements as an ordered combination adds nothing that is not already present when considering the additional elements individually. As a result, independent claims 1 and 18 do not include additional elements that amount to significantly more than the abstract idea under Step 2B.
Claims 4-7 do not include any additional elements beyond those recited with respect to claims 1 and 18. As a result, claims 4-7 do not include additional elements that amount to significantly more than the abstract idea under Step 2B for the same reasons as stated above with respect to claims 1 and 18.
Claims 2-3 and 8-9 include additional elements that do not recite an abstract idea under Step 2A Prong One. The additional elements of claims 2-3 and 8-9 include an artificial intelligence system, artificial intelligence engines, blockchain systems, a server, a communication network, electronic blocks, and a server. The additional elements do not amount to significantly more than the abstract idea because the additional computing elements do no more than generally link the use of the recited abstract idea to a particular technological environment. Further, looking at the additional elements as an ordered combination adds nothing that is not already present when considering the additional elements individually. As a result, claims 2-3 and 8-9 do not include additional elements that amount to significantly more than the abstract idea under Step 2B.
Therefore, the claims are directed to an abstract idea without additional elements amounting to significantly more than the abstract idea. Accordingly, claims 1-9 and 18 are rejected under 35 U.S.C. 101 as being directed to non-statutory subject matter.
Claim Rejections - 35 USC § 103
In the event the determination of the status of the application as subject to AIA 35 U.S.C. 102 and 103 (or as subject to pre-AIA 35 U.S.C. 102 and 103) is incorrect, any correction of the statutory basis for the rejection will not be considered a new ground of rejection if the prior art relied upon, and the rationale supporting the rejection, would be the same under either status.
The following is a quotation of 35 U.S.C. 103 which forms the basis for all obviousness rejections set forth in this Office action:
A patent for a claimed invention may not be obtained, notwithstanding that the claimed invention is not identically disclosed as set forth in section 102 of this title, if the differences between the claimed invention and the prior art are such that the claimed invention as a whole would have been obvious before the effective filing date of the claimed invention to a person having ordinary skill in the art to which the claimed invention pertains. Patentability shall not be negated by the manner in which the invention was made.
The factual inquiries set forth in Graham v. John Deere Co., 383 U.S. 1, 148 USPQ 459 (1966), that are applied for establishing a background for determining obviousness under 35 U.S.C. 103 are summarized as follows:
1. Determining the scope and contents of the prior art.
2. Ascertaining the differences between the prior art and the claims at issue.
3. Resolving the level of ordinary skill in the pertinent art.
4. Considering objective evidence present in the application indicating obviousness or nonobviousness.
Claims 1-9 and 18 are rejected under 35 U.S.C. 103 as being unpatentable over Khan et al. (US Pub No. 2024/0144142) (hereinafter Khan et al.) in view of Lee et al. (US Pub No. 2020/0265356) (hereinafter Lee et al.).
Regarding claim 1, Khan in view of Lee discloses a system for evaluating employee performance, comprising:
a. One or more processors positioned onto one or more server grade computers (see Khan, para [0012], wherein machine-learning based worker performance assessment and recommendations is disclosed, the computer system comprising: a memory having processor-readable instructions stored therein; and one or more processors configured to access the memory and execute the processor-readable instructions);
b. Non-transitory computer readable storage medium resident upon one or more said server grade computers (see Khan, para [0012], wherein a memory having processor-readable instructions stored therein; and one or more processors configured to access the memory and execute the processor-readable instructions, which when executed by the one or more processors);
c. A (database management system) resident upon one or more said server grade computers for storing data produced from said computer-implemented system (see Khan, para [0051], wherein System 244 can be configured to process data and information from labor database 238; and para [0110], wherein a read-only memory (ROM), may be, for example, a hard disk drive or a removable storage drive);
d. An artificial intelligence system comprising one or more machine learning algorithms resident upon one or more said server grade computers accessible by said one or more processors, said artificial intelligence system providing instructions that, when executed upon by said one or more data processors, cause said one or more data processors to perform operations (see Khan, para [0062], wherein a CNN may be a deep and feed-forward artificial neural network; and para [0012], wherein machine-learning based worker performance assessment and recommendations is disclosed, the computer system comprising: a memory having processor-readable instructions stored therein; and one or more processors configured to access the memory and execute the processor-readable instructions) comprising;
i. Receiving real-time work goals and assignments for one or more employees (see Khan, para [0072], wherein Fig. 6B is an example user interface dashboard 620 associated with the worker performance database of EPM control tower 210a-n. As shown, dashboard 620 can present information related to worker performance. Through dashboard 620, a user can observe real-time performance, how current performance measures against objective planned performance goal(s), object measures of worker engagement, and or the like. In some aspects, one or more alerts can be presented or otherwise pushed onto dashboard 620 instructing a user (e.g., an employee, a manager, etc.) to take one or more corrective actions to improve productivity (e.g., return to a task, improve one criteria of a task that is lacking, improve engagement in an area of engagement, etc.) of one or more operational disruptions);
ii. Calculating employee performance data based on the completion of said real-time work goals and assignments (see Khan, para [0115], wherein the data analysis may determine performance scores for each of the one or more performance categories, and calculate an overall worker performance score. The worker performance score for each category of this disclosure may be displayed on a dashboard and/or related scorecards. In some aspects, one or more functions are used to calculate scores (e.g., assigning a coefficient factor to values of categories such as time on task, time between tasks, number of tasks completed, idle state, etc.); and para [0072], wherein Fig. 6B is an example user interface dashboard 620 associated with the worker performance database of EPM control tower 210a-n. As shown, dashboard 620 can present information related to worker performance. Through dashboard 620, a user can observe real-time performance, how current performance measures against objective planned performance goal(s), object measures of worker engagement, and or the like);
iii. Processing said employee performance data using said machine learning algorithms from said artificial intelligence system to generate a employee performance evaluation (see Khan, para [0062], wherein a CNN may be a deep and feed-forward artificial neural network; and para [0012], wherein machine-learning based worker performance assessment and recommendations is disclosed, the computer system comprising: a memory having processor-readable instructions stored therein; and one or more processors configured to access the memory and execute the processor-readable instructions); and
iv. Storing said employee performance evaluation onto said (database management system), Wherein said generated performance evaluations are usable for predicting/improving employee performance (see Khan, para [0062], wherein a CNN may be a deep and feed-forward artificial neural network; and para [0012], wherein machine-learning based worker performance assessment and recommendations is disclosed, the computer system comprising: a memory having processor-readable instructions stored therein; and one or more processors configured to access the memory and execute the processor-readable instructions).
Khan et al. fails to explicitly disclose a blockchain system.
Analogous art Lee discloses a blockchain system resident upon one or more said server grade computers for storing data produced from said computer-implemented system (see Lee, para [0034], wherein execution of smart contracts in a blockchain-based system; paras [0205]-[0206], wherein computer processor that is specially configured (e.g., using software), the computer processor……..a memory device to which it is communicatively coupled. A further hardware module may then, at a later time, access the memory device to retrieve and process the stored output).
Khan directed to a system for determining that the worker score is equal to or greater than the target score. Lee directed to providing provide insight into how the probabilistic assessment was generated. It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to have modified the teachings of Khan, regarding the System For Worker Recommendations, to have included a blockchain system resident upon one or more said server grade computers for storing data produced from said computer-implemented system because both inventions teach improving information security. Further, 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.
Regarding claim 2, Khan in view of Lee discloses the system for evaluating employee performance of Claim 1 wherein said artificial intelligence system, as set forth above with claim 1.
Khan et al. fails to explicitly disclose decentralized.
Analogous art Lee discloses decentralized (see Lee, para [0087], wherein consensus-based decentralized exchange, smart contracts, and crypto tokens)).
One of ordinary skill in the art would have recognized that applying the known technique of Lee would have yielded predictable results and resulted in an improved system for the same reasons as stated above with respect to claim 1.
Regarding claim 3, Khan in view of Lee discloses the system for evaluating employee performance of Claim 2 wherein said decentralized artificial intelligence system comprises two or more artificial intelligence engines (see Khan, para [0062], wherein a CNN may be a deep and feed-forward artificial neural network).
Khan et al. fails to explicitly disclose two or more artificial intelligence engines in a decentralized artificial intelligence network.
Analogous art Lee discloses two or more artificial intelligence engines in a decentralized artificial intelligence network (see Lee, para [0087], wherein consensus-based decentralized exchange, smart contracts, and crypto tokens); and para [0046], wherein the application servers 118 host one or more AI governance platform 120, including its applications and extensions).
One of ordinary skill in the art would have recognized that applying the known technique of Lee would have yielded predictable results and resulted in an improved system for the same reasons as stated above with respect to claim 1.
Regarding claim 4, Khan in view of Lee discloses the system for evaluating employee performance of Claim 1 wherein said artificial intelligence system comprises the steps of:
i. Forming a data set from the collected employee data (see Khan, para [0011], wherein aggregating and analyzing data from a plurality of connected warehouse service systems, a plurality of connected performance management systems, a connected labor management system (LMS), and a gateway device);
ii. Producing an estimate about one or more patterns in the data set (see Khan, para [0115], wherein smart worker performance scoring and evaluation of a job site (e.g., one or more warehouses), whereby information from sensors and/or connected worker computing devices may provide dynamic data about job performance (e.g., productivity of worker(s), task productivity, production productivity, etc.), a processor and database(s); and para [0052], wherein perform statistical analysis of worker trends including assigned tasks, event datasets to derive insights on worker performance considering the nature of work, skillset, criticality, labor intensity, etc. In some aspects, the trend model can classify data on a variety of key performance parameters to generate reports, dashboards, and insights that can be presented to users); and
iii. Making a prediction about the data set (see Khan, para [0087], wherein scorecard 914 can also include a trend number of idle workers as well as predicted idle workers according to current information aggregated from corresponding sensor and worker computing devices in comparison to idle worker historical information);
Regarding claim 5, Khan in view of Lee discloses the system for evaluating employee performance of claim 4 wherein said artificial intelligence system further comprises the step of evaluating said prediction about said data set (see Khan, para [0115], wherein smart worker performance scoring and evaluation of a job site (e.g., one or more warehouses), whereby information from sensors and/or connected worker computing devices may provide dynamic data about job performance (e.g., productivity of worker(s), task productivity, production productivity, etc.), a processor and database(s); and para [0052], wherein perform statistical analysis of worker trends including assigned tasks, event datasets to derive insights on worker performance…….).
Regarding claim 6, Khan in view of Lee discloses the system for evaluating employee performance of claim 5 wherein said artificial intelligence system further comprises the step of optimizing said prediction for accuracy (see Khan, paras [0052] & [0058], wherein Fig. 3 is a flowchart illustrating a method 300 for optimizing operations of a job site. In step 310, the method can include providing visibility into real-time workforce productivity. In step 320, the method can include viewing worker productivity by location across functional areas. In step 330, the method can include providing worker recommendations to return to a worker plan. In step 340, the method can include providing tools to reallocate workers, assignment tasks, react to unplanned events. In step 350, the method can include measuring the impact of changes to make persistent improvement and trend to an optimized job site)…….the trend model can perform statistical analysis of worker trends including assigned tasks, event datasets to derive insights on worker performance).
Regarding claim 7, Khan in view of Lee discloses the system for evaluating employee performance of Claim 6 further employing said artificial intelligence to analyze employee performance trends and patterns from said stored performance metrics (see Khan, para [0052], wherein the trend model can perform statistical analysis of worker trends including assigned tasks, event datasets to derive insights on worker performance considering the nature of work, skillset, criticality, labor intensity, etc. In some aspects, the trend model can classify data on a variety of key performance parameters to generate reports, dashboards, and insights that can be presented to users; and para [0067], wherein a determination may be made of a worker score based on the value of the at least one worker performance metric and the at least one worker performance parameter, the metrics and parameters being described above with regards to FIG. 4. At step 530, an optimal worker score ( or target score) is determined based on the value of the at least one worker performance metric and the at least one worker performance parameter).
Regarding claim 8, Khan in view of Lee discloses the system for evaluating employee performance of Claim 1 wherein said de-centralized artificial intelligence system adjusts and updates said predefined work goals and assignments using artificial intelligence analysis of each said artificial intelligence engine and said gathered historical employee performance data (see Khan, para [0066], wherein determination is based on historical data analysis, as well as a specific working model developed to replicate the worker performance and contributing parameters in assessment model 410; para [0069], wherein the assessment also incorporates worker feedback to further tune the recommendations. The outcome of this assessment model is used in future assessments to add new meta data or to adjust the significance of existing meta data to worker performance in a continuously updated process; para [0103], wherein scorecard….a predetermined performance parameter (e.g., frequency of updating worker preferences, frequency that worker checks their performance score, frequency of worker's career trajectory, frequency of worker performing one or more of the same tasks, etc.); and para [0072], wherein Fig. 6B is an example user interface dashboard 620 associated with the worker performance database of EPM control tower 210a-n. As shown, dashboard 620 can present information related to worker performance. Through dashboard 620, a user can observe real-time performance, how current performance measures against objective planned performance goal(s), object measures of worker engagement, and or the like……….).
Regarding claim 9, Khan in view of Lee discloses the system for tracking employee performance of Claim 8 wherein said de- centralized artificial intelligence system generates dynamic recommendations for performance improvement based upon said artificial intelligence analysis of employee performance data (see Khan, para [0078], wherein workforce analytic modules 815 can also include one or more worker performance dashboards 823 and improvement recommendations 825; and para [0062], wherein a CNN may be a deep and feed-forward artificial neural network).
Regarding claim 18, Khan in view of Lee discloses a process for tracking employee performance, comprising:
a. Collecting employee performance data from one or more data sources and storing said employee performance data (see Khan, para [0011], wherein the trained machine-learning model determines the plurality of worker performance metrics by aggregating and analyzing data from a plurality of connected warehouse service systems, a plurality of connected performance management systems, a connected labor management system (LMS), and a gateway device; and para [0034], wherein collect data from the coupled one or more sensors, perform computations and/or analysis of the collected data, store the collected and/or analyzed data in memory, and provide the collected and/or analyzed data to one or more connected edge systems and/or gateway system);
b. Storing said employee performance data (see Khan, para [0012], wherein machine-learning based worker performance assessment and recommendations is disclosed, the computer system comprising: a memory having processor-readable instructions stored therein; and one or more processors configured to access the memory and execute the processor-readable instructions);
c. Providing a computer system having one or more (see Khan, para [0012], wherein machine-learning based worker performance assessment and recommendations, the computer system comprising: a memory having processor-readable instructions stored therein; and one or more processors configured to access the memory and execute the processor-readable instructions)
d. Providing an artificial intelligence system for analysis of said employee performance data (see Khan, para [0062], wherein a CNN may be a deep and feed-forward artificial neural network; and para [0011], wherein the trained machine-learning model determines the plurality of worker performance metrics by aggregating and analyzing data from a plurality of connected warehouse service systems, a plurality of connected performance management systems, a connected labor management system (LMS), and a gateway device);
e. Providing a performance evaluator to analyze said employee performance data (see Khan, para [0031], wherein identifying and assessing worker performance, training needs, strengths and weaknesses and appropriately motivating, training, and re-allocating jobs may help maintain a healthy and efficient work environment and increase productivity and profitability. In such large operations, it may be difficult or inefficient for supervisors or managers to monitor in real time the performance of employees owing to the nature and size of operations. Workers may be working part-time, full-time or as contractors, and may work multiple sites and shifts with a variety of managers and supervisors, such that it is difficult for supervisors to have meaningful engagement with individual workers for purposes of addressing worker needs. Such lack of engagement may result in unproductive or unhappy workers leading to loss of productivity and reductions in retention. To address this gap, there is a need for an automated system to assess the worker performance, environment, engagement levels and recommend improvements for better worker engagement and to improve productivity);
f. Providing at least one or more predictions based upon analysis of said employee performance data (see Khan, para [0087], wherein a running calculation of total time lost to idle time across a period of time (e.g., the past day, the past week, the current shift, the past month, etc.). Scorecard 914 can also include a trend number of idle workers as well as predicted idle workers according to current information aggregated from corresponding sensor and worker computing devices in comparison to idle worker historical information); and
g. Using said one or more predictions to execute instructions for employee mitigation (see Khan, para [0087], wherein Scorecard 914 can also include a trend number of idle workers as well as predicted idle workers according to current information aggregated from corresponding sensor and worker computing devices in comparison to idle worker historical information; and para [0072], wherein determining the right worker for a particular task based on the worker's profile and/or preferences, including job location or zone with job location, physical demand, temperature, shift, seniority, performance against the necessary tasks etc. to inform recommendations to move workers from area to another to mitigate attrition and maximize worker satisfaction).
Khan et al. fails to explicitly disclose a blockchain system.
Analogous art Lee discloses a blockchain system (see Lee, para [0034], wherein execution of smart contracts in a blockchain-based system).
Khan directed to a system for determining that the worker score is equal to or greater than the target score. Lee directed to providing provide insight into how the probabilistic assessment was generated. It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to have modified the teachings of Khan, regarding the System For Worker Recommendations, to have included a blockchain system because both inventions teach improving information security. Further, 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.
Claims 10-17 are rejected under 35 U.S.C. 103 as being unpatentable over Khan et al. (US Pub No. 2024/0144142) (hereinafter Khan et al.), in view of Lee et al. (US Pub No. 2020/0265356) (hereinafter Lee et al.), and further in view of Smith et al. (US Pub No. 2019/0132350) (hereinafter Smith et al.).
Regarding claim 10, Khan in view of Lee discloses the system for evaluating employee performance of Claim 1 wherein said blockchain system for storing and managing employee-related performance data, as set forth above with claim 1, comprises:
a. A plurality of server grade computers interconnected through a communication network (see Khan, paras [0041]-[0042] & [0050], wherein EPM control tower 210a-n and networked warehouse system of record 220a-n can reside in a cloud based computing system 242 (e.g., a cloud computing network, one or more remote servers) and be communicatively coupled to a data transformation and integration layer 230);
b. One or more electronic blocks for data storage positioned within said (database management system) (see Khan, para [0051], wherein System 244 can be configured to process data and information from labor database 238; and para [0110], wherein a read-only memory (ROM), may be, for example, a hard disk drive or a removable storage drive);
c. A (distributed environment) maintained across said plurality of server grade computers, wherein each said electronic block within said (database management system) includes a time stamped record of employee-related performance data (see Khan, para [0108], wherein a sequential process, some of the operations may in fact be performed in parallel, concurrently, and/or in a distributed environment, and with program code stored locally or remotely for access by single or multi-processor machines; para [0051], wherein System 244 can be configured to process data and information from labor database 238; and para [0110], wherein a read-only memory (ROM), may be, for example, a hard disk drive or a removable storage drive; and para [0077], wherein a dashboard 710 showing real-time statistics about the total number of workers available and the total number of workers idle, the percentage of idle time per worker, and the total amount of idle time accumulated so far for the work period);
d. A employee data input module configured to receive employee-related performance data from multiple sources, said data including performance metrics and associated metadata (see Khan, para [0059], wherein the system 400 for worker assessment and recommendations includes an assessment model 410 that receives as inputs worker performance data 420, a worker profile 430, and meta data 440 regarding the working conditions. The worker performance data 420 may include a worker score from a worker scorecard as described in FIGS. 6-10……The parameters from the worker profile 430 and the meta data 440 are given coefficients and values and are likewise input into the assessment model 410);
e. A (database validation) integrated into said (database management system), said (database validation) governing the validation and addition of new said electronic blocks to said (database management system) (database management system) (see Khan, para [0052], wherein database 232 can be configured for data validation and modification for incoming telemetry or attributes before saving to the database; and para [0051], wherein System 244 can be configured to process data and information from labor database 238).
Khan et al. fails to explicitly disclose a blockchain system; a distributed ledger maintained across said plurality of server grade computers, wherein each said electronic block within said blockchain system; a consensus mechanism integrated into said blockchain system, said consensus mechanism governing the validation and addition of new said electronic blocks to said blockchain, and ensuring that only authorized participants are allowed to validate and commit said electronic blocks to said blockchain system.
Analogous art Lee discloses a blockchain system resident upon one or more said server grade computers for storing data produced from said computer-implemented system (see Lee, para [0034], wherein execution of smart contracts in a blockchain-based system; paras [0205]-[0206], wherein computer processor that is specially configured (e.g., using software), the computer processor……..a memory device to which it is communicatively coupled. A further hardware module may then, at a later time, access the memory device to retrieve and process the stored output);
Analogous art Lee discloses a distributed ledger maintained across said plurality of server grade computers, wherein each said electronic block within said blockchain system includes a time stamped record of employee-related performance data (see Lee, para [0192], wherein referring to Fig. 22B architecture of the AI platform. The AI platform includes a ledger service 2214 that is configured to implement record blocks and time record blocks; para [0034], wherein execution of smart contracts in a blockchain-based system; and para [0179], wherein one of indicators to gauge employees'/contractors' satisfaction level is to monitor their performance…….(1)
Someone whose performance rating is below-average……or (2) Someone whose performance rating drop from the best performance to average performance);
Analogous art Lee discloses a consensus mechanism integrated into said blockchain system, said consensus mechanism governing the validation and addition of new said electronic blocks to said blockchain, and ensuring that only authorized participants are allowed to validate and commit said electronic blocks to said blockchain system (see Lee, para [0090], wherein the multi-threaded blockchain (e.g., such as those related to the consensus mechanism) are addressed in the platform's implementation; para [0098], wherein the various attributes and values may be stored in a code record (e.g., with a unique ID and hash 708) in a code validation blockchain, such as the enhanced blockchain described herein (e.g., in code validation blockchain 712); and para [0094], wherein attempts to address this issue by monitoring the system log and send alerts people based on the log or output from log analysis results in inundating people with many false positives. In order to ensure analytic integrity, more security controls may be added to govern and manage the analytical code……..the disclosed solution proactively and continually validates the analytics/Al's performance and its expected performance. The AI governance platform approaches this in at least the following novel ways to catch or predict anomalies in a more timely and more accurate fashion).
One of ordinary skill in the art would have recognized that applying the known technique of Lee would have yielded predictable results and resulted in an improved system for the same reasons as stated above with respect to claim 1.
Khan et al. and Smith et al. fails to explicitly disclose an encryption and access control module configured to encrypt the employee- related performance data before appending it to a said electronic block in said blockchain system, wherein said access control module enforces predetermined access rights to data based on cryptographic keys.
Analogous art Smith discloses an encryption and access control module configured to encrypt the employee related performance data before appending it to a said electronic block in said blockchain system, wherein said access control module enforces predetermined access rights to data based on cryptographic keys (see Smith, para [0154], wherein an encryption (cryptography) layer 506 can include cryptography used to cipher (encrypt) and de-cipher (decrypt) information by using a mathematical function or algorithm. Encryption can refer to the process of transforming information so it is unintelligible/inaccessible/unreadable to anyone but the intended recipient. Decryption is the process of transforming encrypted information so that it is intelligible/accessible/readable again; and table 7, wherein 1. Inquire with the control owner and determine where information security policies and procedures are documented and available to personnel with access to PII on accessible resources (internal network, shared drive, etc.), 2. Review policies and procedures to confirm they are reviewed/updated at least annually or more frequently if required based on changes in the environment, 3. Inspect the information security policies on the company's internal network and determine they are available and communicated to company personnel with access to PII and include security roles, responsibilities, and procedures. Specifically, privacy policies and operational procedures for the confidentiality and non-disclosure agreements. [Storage Location of PII]; and para [0083], wherein These transactions will manifest as a simple key-value pair, as illustrated below:
{// on the blockchain
_id: 3199444, //a unique identifier
dateTime: 2103323223 //unix timestamp,
transaction; 2193298439843984381212211 //a transaction,
sender: John Doe, //string
receiver: Jane Doe, //string
amount: 10 / /integer}).
Khan directed to a system for determining that the worker score is equal to or greater than the target score. Smith directed to validating of distributed data storage systems. It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to have modified the teachings of Khan, regarding the System For Worker Recommendations, to have included an encryption and access control module configured to encrypt the employee related performance data before appending it to a said electronic block in said blockchain system, wherein said access control module enforces predetermined access rights to data based on cryptographic keys because both inventions teach improving information security. Further, 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.
Regarding claim 11, Khan in view of Lee discloses the system for evaluating employee performance of Claim 10 wherein said blockchain system, as set forth above with claim 1.
Khan et al. fails to explicitly disclose a smart contract module integrated with said blockchain system, said smart contract module comprising one or more programmable contracts that automatically execute predefined actions upon the occurrence of specified conditions within said blockchain system.
Analogous art Lee discloses a smart contract module integrated with said blockchain system, said smart contract module comprising one or more programmable contracts that automatically execute predefined actions upon the occurrence of specified conditions within said blockchain system (see Lee, para [0034], wherein execution of smart contracts in a blockchain-based system; and para [0140]-[0146], wherein the smart contract is wrapped with a special ( e.g., Talisai) wrapper. The wrapper has a data schema ready to capture the identified a data sets with time stamps and communicate them to a message bus. An example of such code wrapping is depicted in FIG. 24A).
One of ordinary skill in the art would have recognized that applying the known technique of Lee would have yielded predictable results and resulted in an improved system for the same reasons as stated above with respect to claim 1.
Regarding claim 12, Khan in view of Lee discloses the system for evaluating employee performance of Claim 11 having a user interface module providing authorized users with access (see Khan, para [0071], wherein Fig. 6A is an example user interface dashboard 610 associated with the worker performance database of EPM control tower 210a-n).
Khan et al. fails to explicitly disclose said blockchain system, allowing said authorized users to submit employee-related performance data, configure access controls, and retrieve performance data using appropriate cryptographic keys.
Analogous art Lee discloses a user interface module providing authorized users with access to said blockchain system, allowing said authorized users to submit employee-related performance data, configure access controls, and retrieve performance data using appropriate cryptographic keys (see Lee, para [0136], wherein user interface includes one or more dynamic user interface elements for drilling down into the AI algorithm, including key constraints, to identify one or more reasons for the violation; para [0034], wherein execution of smart contracts in a blockchain-based system; para [0049], wherein API server 114 the third-party applications may, utilizing information retrieved from the networked system 102, support one or more features or functions on a website hosted by the third party; para [0176], wherein Sub-context 1: Access behavior. Ex: Consecutive log-in or access on critical system or physical access. Example data sources: (1) User ID, Date and time; and para [0140]-[0146], wherein one or more of these things are captured: (a) input to the function and output of the function, (b) state variables, ( c) other meta information about the contract will also be captured, such as comment (e.g., description), data set used (e.g., table names, logical source, field names, dataset index), query used (e.g., any database query, code version and code ID), who (e.g., names of people who contributed to the AI code), other comments ( e.g., description of what the code is meant to do), or cryptographic token standard field(s) (e.g., ERC20 fields)).
One of ordinary skill in the art would have recognized that applying the known technique of Lee would have yielded predictable results and resulted in an improved system for the same reasons as stated above with respect to claim 1.
Regarding claim 13, Khan in view of Lee discloses the system for evaluating employee performance of Claim 12 wherein said blockchain system further comprises a data analytics module integrated with the (database management system), the data analytics module utilizing the stored employee related performance data to generate insights, trends, and reports related to employee performance (see Khan, para [0051], wherein System 244 can be configured to process data and information from labor database 238; para [0115], wherein smart worker performance scoring and evaluation of a job site (e.g., one or more warehouses), whereby information from sensors and/or connected worker computing devices may provide dynamic data about job performance (e.g., productivity of worker(s), task productivity, production productivity, etc.), a processor and database(s); and para [0052], wherein perform statistical analysis of worker trends including assigned tasks, event datasets to derive insights on worker performance considering the nature of work, skillset, criticality, labor intensity, etc. In some aspects, the trend model can classify data on a variety of key performance parameters to generate reports, dashboards, and insights that can be presented to users).
Khan et al. fails to explicitly disclose the blockchain network.
Analogous art Lee discloses said blockchain system further comprises a data analytics module integrated with the blockchain network (see Lee, para [0034], wherein execution of smart contracts in a blockchain-based system; paras [0205]-[0206], wherein computer processor that is specially configured (e.g., using software), the computer processor……..a memory device to which it is communicatively coupled. A further hardware module may then, at a later time, access the memory device to retrieve and process the stored output; and para [0216], wherein (or distributed) network environment).
One of ordinary skill in the art would have recognized that applying the known technique of Lee would have yielded predictable results and resulted in an improved system for the same reasons as stated above with respect to claim 1.
Regarding claim 14, Khan in view of Lee discloses the system for evaluating employee performance of Claim 12 wherein configured to execute predefined actions based on said performance metrics (see Khan, para [0089], wherein scorecard 918 provides a prompt, efficient, and results-oriented solution to in real-time determine and present recent-hire worker performance insights and to both provide corrective actions as well as maintain levels of engagement and encouragement for recent hire retention).
Khan et al. fails to explicitly disclose said one or more smart contracts.
Analogous art Lee discloses said one or more smart contracts are configured to execute predefined actions based on said performance metrics (see Lee, para [0088], wherein validates data and the analytic assets (e.g., data assets 610) through consensus and smart-contract mechanisms, can make the effectiveness of an organization's algorithms and data assets visible to a broader community of employees and facilitate collaboration across the organization; and para [0103], wherein key metrics (e.g., feedback score metrics, and customer action vs. roboadvisor recommended actions) are depicted (e.g., for a particular date or range of dates associated with a replay or simulation of a particular scenario)).
One of ordinary skill in the art would have recognized that applying the known technique of Lee would have yielded predictable results and resulted in an improved system for the same reasons as stated above with respect to claim 1.
Regarding claim 15, Khan in view of Lee discloses the system for evaluating employee performance of Claim 14 wherein said smart contracts, as set forth above with claim 14 are configured to provide one or more of the following of employee mitigation, employee improvement, employee incentive, manager mitigation, manager improvement, manager incentive, organizational notice, and organizational rapid response (see Khan, para [0031], wherein an automated system to assess the worker performance, environment, engagement levels and recommend improvements for better worker engagement and to improve productivity).
Regarding claim 16, Khan in view of Lee discloses the system for evaluating employee performance of Claim 12 wherein said blockchain system is decentralized, as set forth above with claim 2.
Regarding claim 17, Khan in view of Lee discloses the system for evaluating employee performance of Claim 12, as set forth above with claim 12.
Khan et al. fails to explicitly disclose a system of auditing said blockchain system.
Analogous art Lee discloses a system of auditing said blockchain system (see Lee, para [0147], wherein the smart contract executable runs in a blockchain (or off-chain), the captured data that must be audited and monitored will be communicated to the AI platform, which subsequently creates its own immutable record blocks that could be referenced).
One of ordinary skill in the art would have recognized that applying the known technique of Lee would have yielded predictable results and resulted in an improved system for the same reasons as stated above with respect to claim 1.
Conclusion
The prior arts made of record and not relied upon is considered pertinent to applicant's disclosure. (US Pub No. 2017/0293874; US Pat No. 11,126,949; US Pub No. 2024/0220893; US Pub No. 2015/0269244; US Pat No. 8,311,863; US Pub No. 2022/0292999; US Pub No. 2013/0006883; US Pub No. 2021/0012013; US Pub No. 2020/0005213;