DETAILED ACTION
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 .
Introduction
The following is a final Office Action in response to Applicant’s communications received on August 4, 2026. Claims 1 and 11 have been amended, claim 9 has been canceled, and claim 21 has been added.
Currently claims 1-8 and 10-21 are pending. Claims 1, 11 and 21 are independent.
Response to Amendments
Applicant’s amendments necessitated the new ground(s) of rejection in this Office Action.
Applicant’s amendments to claims 1 and 11 are NOT sufficient to overcome the 35 U.S.C. § 101 rejection as set forth in the previous Office Action. Therefore, the 35 U.S.C. § 101 rejection to claims 1-8 and 10-21 has been maintained.
Response to Arguments
Applicant’s arguments filed on August 4, 2026 have been fully considered but are not persuasive.
In the Remarks on page 13, Applicant’s arguments regarding the 35 U.S.C. § 101 rejection that the amended claims 1 and 11 are no longer susceptible to the characterization (of mental process). Instead, the claims now positively recite “a generative artificial intelligence model served through a model orchestration, the generative artificial intelligence model comprising an embedding and vector store, a feedback store, a fine tuning store, and model guardrails.”
In response to Applicant’s argument, the Examiner respectfully disagrees. Reciting a generative artificial intelligence model or providing a generative artificial intelligence model with an abstract idea is merely adding the words “apply it” or using “a particular machine” with an abstract idea, or mere instructions to implement an abstract idea on a computer. The Supreme Court has repeatedly made clear that merely limiting the field of use of the abstract idea to a particular existing technological environment does not render the claims any less abstract. See Affinity Labs of Texas, LLC v. DirecTV, LLC, 838 F.3d 1253, 1258 (Fed. Cir. 2016). The claims still recite an abstract idea, and the recited additional elements do not integrate the abstract idea into a practical application. For example: “identifying a target entity and a set of entities, aligning organization role profiles of the target entity with a taxonomy, mapping workforce data of the target entity obtained from one or more third party data source to the taxonomy, determining a human-to-machine coefficient associated with the use case” that invoked with observation, evaluation, and judgement, which can be practically performed in the human mind. See Revised 2019 Guidance, 84 Fed. Reg. 50, 54-55 (January 7, 2019). Further, the additional elements of “a computer processor” and “a generative artificial intelligence model” are recited at a high level of generality and merely limiting the field of use of the abstract idea to a particular existing technological environment because the additional elements fail to reflect any improvement to the functioning of the processor itself or the generative AI model.
For generative AI or machine learning models to integrate an abstract idea into a practical application, they must be initially trained on specific algorithms based on large datasets to learn the patterns, structures, and relationships needed to create new content. Then, performed a task using the trained models based on algorithms and corresponding datasets, and finally updated or fine-tuned the models using feedback/operational data reflecting current patterns and environmental changes. Here, neither the claims nor the Specification detail how the generative AI is trained, nor does it explain how the generative AI is used to perform a particular function. Therefore, the additional elements do not integrate the abstract idea into a practical application.
In the Remarks on page 14, Applicant’s arguments regarding the 35 U.S.C. § 101 rejection that even arguendo that the claims recite an abstract idea at Prong One, amended claims 1 and 11 integrate any such exception into a practical application at Prong Two.
In response to Applicant’s argument, the Examiner respectfully disagrees. In order for a claim to integrate the exception into a practical application, the additional claimed elements must, for example, improve the functioning of a computer or any other technology or technical field (see MPEP § 2106.05(a)), apply the judicial exception with a particular machine (see MPEP § 2106.05(b)), affect a transformation or reduction of a particular article to a different state or thing (see MPEP § 2106.05(c)), or apply or use the judicial exception in some other meaningful way beyond generally linking the use of the judicial exception to a particular technological environment (see MPEP § 2106.05(e)). See Revised 2019 Guidance.
Here, as discussed above, the claims recited the additional elements of “a computer processor” and “a generative artificial intelligence model” at a high level of generality and merely limiting the field of use of the abstract idea to a particular existing technological environment because the additional elements fail to reflect any improvement to the functioning of the processor itself or the generative AI model. Thus, the claimed additional elements do not improve any functioning of a computer or another technology. See Customedia Techs., LLC v. Dish Network Corp., 951 F.3d 1359, 1365 (Fed. Cir. 2020) (“The only improvements identified in the specification are generic speed and efficiency improvements inherent in applying the use of a computer to any task. . . . This is not an improvement in the functioning of the computer itself.”). Therefore, the additional elements do not integrate the abstract idea into a practical application.
With respect to the court reviewed cases of Ex Parte Desjardins and McRo, the rejected claims do not adhere to the same fact pattern seen in Ex Parte Desjardins and McRO. In Ex Parte Desjardins, the claimed invention was a methos of training a machine learning model on a series of tasks where machine learning model learn new tasks while protecting knowledge about previous task to overcome the problem of catastrophic forgetting. See Appeal No. 2024-000567, at 9 (PTAB Sep. 26, 2025); In the McRO, the claimed improvement [was] allowing computers to produce 'accurate and realistic lip synchronization and facial expressions in animated characters' that previously could only be produced by human animators.” See McRO, Inc. v. Bandai Namco Games America Inc., 120 USPQ2d 1091 (Fed. Cir. 2016). However, the Applicant’s claims and specification is silent with respect to description of any technique/way to solve any specific computer problem. Additionally, Applicant does not provide any comparison to the applicant’s claims with Ex Parte Desjardins and McRO.
In the Remarks on page 17, Applicant argues that none of Portnoy, Hanley, or Cella, alone or in combination, teaches or suggests the human-to-machine-coefficient element(s) now affirmatively recited in amended claim 1. However, Applicant’s argument is directed to the newly amended claims, and therefore, the newly amended claims will be fully addressed in this Office Action.
Claim Rejections – 35 USC § 112
The following is a quotation of 35 U.S.C. 112(a):
(a) IN GENERAL.—The specification shall contain a written description of the invention, and of the manner and process of making and using it, in such full, clear, concise, and exact terms as to enable any person skilled in the art to which it pertains, or with which it is most nearly connected, to make and use the same, and shall set forth the best mode contemplated by the inventor or joint inventor of carrying out the invention.
The following is a quotation of 35 U.S.C. 112 (pre-AIA ), first paragraph:
The specification shall contain a written description of the invention, and of the manner and process of making and using it, in such full, clear, concise, and exact terms as to enable any person skilled in the art to which it pertains, or with which it is most nearly connected, to make and use the same and shall set forth the best mode contemplated by the inventor of carrying out his invention.
Claims 1-8 and 10-21 are rejected under 35 U.S.C. 112(a) or 35 U.S.C. 112 (pre-AIA ), first paragraph, as failing to comply with the written description requirement. The claims contain subject matters which were not described in the specification in such a way as to reasonably convey to one skilled in the relevant art that the inventor or a joint inventor, or for pre-AIA the inventor(s), at the time the application was filed, had possession of the claimed invention. The subject matters which are not in the original specification is as follows:
Regarding claims 1, 11 and 21, the claims recite “a split between a machine proportion of the activity”, “a remaining human proportion of the activity”, and “applying the machine proportion of the human-to-machine coefficient to the number of workers”. The newly added limitations appear to constitute new matter. Applicant did not point out, nor was Examiner able to find, any support for these newly added limitations in the specification as originally filed. Applicant is required to cancel the new matter throughout the application in the reply to this Office Action..
Dependent claims 2-8, 10, and 12-20 are also rejected for the same reasons as each depends on the rejected claims.
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 1-8 and 10-21 are 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 applicant regards as the invention.
Regarding claims 1, 11 and 21, the claims recite the term “performable” is a relative term which renders the claims indefinite. The term “performable” is not defined by the claims, the specification does not provide a standard for ascertaining the requisite degree, and one or ordinary skill in the art would not be reasonably apprised of the scope of the invention.
Dependent claims 2-8, 10, and 12-20 are also rejected for the same reasons as each depends on the rejected claims.
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 1-8 and 10-21 are rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more.
As per Step 1 of the subject matter eligibility analysis, it is to determine whether the claim is directed to one of the four statutory categories of invention, i.e., process, machine, manufacture, or composition of matter.
In this case, claims 1-8 and 10 are directed to a system comprising a computer processor, which falls within the statutory category of a machine. Claims 11-20 are directed to a method for implementing a Generative AI Opportunity Assessment Tool, which falls within the statutory category of a process. Claims 21 is directed to a non-transitory computer-readable medium containing program instructions, which falls within the statutory category of a product.
In Step 2A of the subject matter eligibility analysis, it is to “determine whether the claim at issue is directed to a judicial exception (i.e., an abstract idea, a law of nature, or a natural phenomenon). Under this step, a two-prong inquiry will be performed to determine if the claim recites a judicial exception (an abstract idea enumerated in the 2019 Guidance), then determine if the claim recites additional elements that integrate the exception into a practical application of the exception. See 2019 Revised Patent Subject Matter Eligibility Guidance (2019 Guidance), 84 Fed. Reg. 50, 54-55 (January 7, 2019).
In Prong One, it is to determine if the claim recites a judicial exception (an abstract idea enumerated in the 2019 Guidance, a law of nature, or a natural phenomenon).
Taking claim 1 as representative, the claim recites the limitations of “identify a target entity and a set of peer entities, generate a baseline assessment of a financial opportunity, align organization role profiles of the target entity with a taxonomy that classifies work activities and processes, map workforce data of the target entity obtained from one or more third party data source to the taxonomy, fine-tune the generative artificial intelligence model by integrating the workforce data…, apply a unique set of use case, identify a set of organizational roles, determine a human-to-machine coefficient associated with the user case, generate a productivity estimate at an activity level based one workforce data, determine an impact comprising a number of workers and total salaries, generate a Generative AI driven workforce capacity value defined by role and function and supplemented with relevant use-cases, generate a current state assessment of existing technology and skills within the target entity relating to Generative AI wherein the current state assessment is based on third party workforce salary and skill data at a role level, apply the assessment model to the target entity’s workforce data to determine an appropriate change management relating to technology and workforce shaping, and identify a workforce capacity opportunity for the target entity”; dependent claims 2-8 and 10 further narrowing and describing the attributes of claim 1. None of the limitations recites technological implementation details for any of these steps, but instead recite only results desired by any and all possible means. The limitations, as drafted, are directed to processes, under their broadest reasonable interpretation, cover performance of the limitations in the mind but for the recitation of generic computer components. That is, other than reciting “a computer processor” configured to, nothing in the claim elements precludes the steps from practically being performed in the mind (including an observation, evaluation, judgment, opinion), or by a human using a pen and paper. For example, the claim encompasses a person can manually identify a target entity and a set of peer entities, generate a baseline assessment…, determine an impact, determine an appropriate change management relating to the technology and workforce shaping, and identify a workforce capacity opportunity for the target entity”, which fall within the “mental processes” grouping. The mere nominal recitation of “a computer processor” and “an interactive user interface” do not take the claim out of the mental processes grouping. See Under the 2019 Guidance, 84 Fed. Reg. 52. Accordingly, the claims recite an abstract idea, and the analysis is proceeding to Prong Two.
In Prong Two, it is to determine if the claim recites additional elements that integrate the exception into a practical application of the exception.
Beyond the abstract idea, the claims recite the additional elements of “an interactive user interface”, “a generative artificial intelligence model”, and “a computer processor”. The Specification describes that “The term “processor” encompasses all apparatus, devices, and machines for processing data, including by way of example a programmable processor, a computer, or multiple processors or computers” (See ¶ 116), and examples of user interface as shown in Fig. 3-11. When given the broadest reasonable interpretation and in light of the Specification, these additional elements are no more than generic computer components. The additional elements are recited at a high level of generality and amount to no more than adding the words “apply it” or using “a particular machine” with an abstract idea, or mere instructions to implement the abstract idea on a computer. Thus, merely adding a generic computer, generic computer components, or programmed computer to perform generic computer functions does not automatically overcome an eligibility rejection. Alice Corp. Pty. Ltd. V. CLS Bank Int’l, 134 S. Ct. 2347, 2358-59, 110 USPQ2d 1976, 1983-84 (2014). Again, automating an abstract process does not convert it into a practical application. See also Bancorp Servs., L.L.C. v. Sun Life Assurance Co. of Canada (U.S.), 687 F.3d 1266, 1278 (Fed. Cir. 2012) (A computer “employed only for its most basic function . . . does not impose meaningful limits on the scope of those claims.”). The Federal Circuit has also indicated that mere automation of manual processes or increasing the speed of a process where these purported improvements come solely from the capabilities of a general-purpose computer are not sufficient to show an improvement in computer-functionality. FairWarning IP, LLC v. Iatric Sys., 839 F.3d 1089, 1095, 120 USPQ2d 1293, 1296 (Fed. Cir. 2016). However, simply implementing the abstract idea on a generic computer does not integrate the abstract idea into a practical application because it does not impose any meaningful limits on practicing the abstract idea. Further, nothing in the claims that reflects an improvement to the functioning of a computer itself or another technology, effects a transformation or reduction of a particular article to a different state or thing, or applies or uses 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 effect designed to monopolize the exception. Therefore, the additional elements do not integrate the judicial exception into a practical application. The claims are directed to an abstract idea, the analysis is proceeding to Step 2B.
In Step 2B of Alice, it is "a search for an ‘inventive concept’—i.e., an element or combination of elements that is ‘sufficient to ensure that the patent in practice amounts to significantly more than a patent upon the [ineligible concept’ itself.’” Id. (alternation in original) (quoting Mayo Collaborative Servs. v. Prometheus Labs., Inc., 132 S. Ct. 1289, 1294 (2012)).
The claims as described in Prong Two above, nothing in the claims that integrates the abstract idea into a practical application. The same analysis applies here in Step 2B.
Beyond the abstract idea, the claims recite the additional elements of “an interactive user interface”, “a generative artificial intelligence model”, and “a computer processor”. The Specification describes that “The term “processor” encompasses all apparatus, devices, and machines for processing data, including by way of example a programmable processor, a computer, or multiple processors or computers” (See ¶ 116), and examples of user interface as shown in Fig. 3-11. When given the broadest reasonable interpretation and in light of the Specification, these additional elements are no more than generic computer components. The additional elements are recited at a high level of generality and merely invoked as tools to perform the generic computer functions including receiving, manipulating, and transmitting data over a network. However, implementing on a generic computer for performing generic computer functions have been recognized by the courts as merely well-understood, routine, and conventional functions of generic computers. See MPEP 2106.05 (d) (II) (Receiving or transmitting data over a network, e.g., using the Internet to gather data, Symantec, 838 F.3d at 1321, 120 USPQ2d at 1362 (utilizing an intermediary computer to forward information); buySAFE, Inc. v. Google, Inc., 765 F.3d 1350, 1355, 112 USPQ2d 1093, 1096 (Fed. Cir. 2014) (computer receives and sends information over a network); RecogniCorp, LLC v. Nintendo Co., 855 F.3d 1322, 1326-27, 122 USPQ2d 1377, 1379-80 (Fed. Cir. 2017) (claim reciting multiple abstract ideas, i.e., the manipulation of information through a series of mental steps and a mathematical calculation, was held directed to an abstract idea)). Thus, simply implementing the abstract idea on a generic computer for performing generic computer functions do not amount to significantly more than the abstract idea. (MPEP 2106.05(a)-(c), (e-f) & (h)).
For the foregoing reasons, claims 1-8 and 10 cover subject matter that is judicially-excepted from patent eligibility under § 101 as discussed above, the other claims 11-20 parallel claims 1-8 and 10—similarly cover claimed subject matter that is judicially excepted from patent eligibility under § 101.
Therefore, the claims as a whole, viewed individually and as a combination, do not provide meaningful limitations to transform the abstract idea into a patent eligible application of the abstract idea such that the claims amount to significantly more than the abstract idea itself. The claims are not patent eligible.
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.
Claims 1-8 and 10-21 are rejected under 35 U.S.C. 103 as being unpatentable over Portnoy et al., (US 2019/0066011, hereinafter: Portnoy), and in view of Hanley, (US 2014/0180706, hereinafter: Hanley), and further in view of Cella et al., (US 2025/0384341, hereinafter: Cella), and Liu (CN 114757530).
(Non-US patent references are cited by page number on the document unless they don’t have page numbers, then cited by PDF page number)
Regarding claim 1, Portnoy discloses a system for implementing a Generative AI Opportunity Assessment Tool comprising:
an input configured to access one or more data sources that store and manage workforce data for at least one target entity (see ¶ 10, ¶ 56-57, ¶ 69, ¶ 166-168, ¶ 353);
an interactive user interface that communicates with one or more user devices via a communication network (see Fig. 3(b); ¶ 10, ¶ 57, ¶ 60, ¶ 117, ¶ 354);
a computer processor coupled to the input, the interactive user interface, the one or more data sources, and the generative artificial intelligence model, the computer processor further configured to (see ¶ 57, ¶ 87, ¶ 160, ¶ 351, claim 16):
identify a target entity and a set of peer entities (see ¶ 56, ¶ 160-168);
generate a productivity estimate at an activity level based on workforce data relevant to the target entity and the human-to-machine coefficient (see Fig. 7(b); ¶ 87, ¶ 227, ¶ 239, ¶ 288);
determine an impact comprising a number of workers and total salaries (see Fig. 4(d); Fig. 6; ¶ 257-259);
generate a current state assessment of existing technology and skills within the target entity relating to Generative AI wherein the current state assessment is based on third party workforce salary and skills data at a role level (see Fig. 6; Fig. 7(j), pg. 2; ¶ 90-96, ¶ 121, ¶ 272-268, ¶ 293, ¶ 334);
apply the fine-tuned model to the target entity’s workforce data to determine an appropriate change management relating to technology and workforce shaping (see ¶ 8, ¶ 73, ¶ 87, ¶ 247, ¶ 334, ¶ 334-340); and
identify a workforce capacity opportunity for the target entity (see ¶ 49-54, ¶ 228-233, ¶ 332-334 ).
Portnoy does not explicitly disclose the following limitations; however, Hanley in an analogous art for human service management discloses
generate a baseline assessment, by applying an assessment model comprising the generative artificial intelligence model, of a financial opportunity for the target entity in leveraging Generative AI at scale (see ¶ 4, ¶ 54, ¶ 106, ¶ 121, claim 9).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify the system of Portnoy to include teaching of Hanley in order to gain the commonly understood benefit of such adaption, such as providing the benefit of enhancing computational efficiency, in turn of operational efficiency. Since the combination of 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.
Portnoy discloses the workforce management system may evaluate and validate managers’ and employees’ survey responses against current levels and against each other using artificial intelligence (AI) and natural language processing techniques (see ¶ 158-164); and the computer may compute the organizational structure based on alignment of the activities or competencies of two suborganizations of the group (see ¶ 10); and the workforce demographic analysis may access an organization’s health and key metrics include employee-contractor composition and ratios and influences with management organization develop strategy, attrition trend analysis…and alignment of employee’s goals with the organization’s mission (see ¶ 166).
Portnoy and Hanley do not explicitly disclose the following limitations; however, Cella in an analogous art for training artificial intelligence models discloses
a generative artificial intelligence model served through a model orchestration, the generative artificial intelligence model comprising an embedding and vector store, a feedback store, a fine tuning store, and model guardrails (see ¶ 2475, ¶ 2524, ¶ 2805, ¶ 2814); and
align organization role profiles of the target entity with a taxonomy that classifies work activities and processes (see ¶ 304-307, ¶ 409, ¶ 1367, ¶ 1755, ¶ 2773);
map workforce data of the target entity obtained from one or more third party data sources to the taxonomy (see ¶ 148, ¶ 412, ¶ 429, ¶ 631, ¶ 899, ¶ 1367);
fine-tune the generative artificial intelligence model by integrating the workforce data of the target entity to generate a fine-tuned model that generates insights specific to the target entity (see ¶ 2489, ¶ 2531, ¶ 2536, ¶ 2542, ¶ 2549-2551, ¶ 2756); and
applying a unique set of use cases, identify a set of organizational roles that illustrate Generative AI agents of change (see ¶ 28, ¶ 32-34, ¶ 732, ¶ 796, ¶ 2492, ¶ 2565);
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify the system of Portnoy and in view of Hanley to include the teaching of Cella in order to gain the commonly understood benefit of such adaption, such as providing the benefit by adapting new technologies to improve the accuracy of data processing. Since the combination of 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.
Cella discloses a smart contract may split between the licensing athlete and split the funds according to a proportion of 60/20/20, whereby 60% of the funds for student, 20% of the funds for the athletic department, and 20% of the funds for the student athlete’s teammates (see ¶ 498); and accessing worker data including human resource database information including the salaries of various workers, information about the rates charged by service workers or other contractors (see ¶ 923), and inputting a set of data sources to the generative AI (see ¶ 2506).
Portnoy, Hanley and Cella do not explicitly disclose the following limitations; however, in an analogous art for processing production data discloses
determine, for each use case of the unique set of use cases, a human-to-machine coefficient associated with the use case, the human-to-machine coefficient representing, for an activity associated with the use case (see Abstract; pg. 4, ¶ 1-5; pg. 9, ¶ 4), a split between a machine proportion of the activity that is performable by the generative artificial intelligence model and a remaining human proportion of the activity (see pg. 10, ¶ 4; pg. 16, ¶ 6-8; pg. 25, claim 6);
generate a Generative AI driven workforce capacity value defined by role and function and supplemented with relevant use-cases by applying the machine proportion of the human-to-machine coefficient to the number of workers performing the activity and an average salary of the number of workers to compute a monetary opportunity and a corresponding percentage of time saved (see pg. 4, ¶ 3-5; pg. 8, ¶ 5-8; pg. 9, ¶ 1-4; pg. 15, ¶ 10-13; pg. 22, ¶ 1; pg. 23, ¶ 1, ¶ 10).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify the system of Portnoy and in view of Hanley and Cella to include the teaching of Liu in order to gain the commonly understood benefit of such adaption, such as providing the benefit of an additional layer of data analysis and resulting in a more focus solution. Since the combination of 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, Portnoy discloses the system of claim 1, wherein the workforce capacity opportunity comprises summary analytic findings and analytic details (see Fig. 3a-g; ¶ 53-54, ¶ 71, ¶ 254, ¶ 322, ¶ 339).
In addition, claim 2 merely describes the attributes of the workforce capacity opportunity is directed to nonfunctional descriptive material because they cannot exhibit any functional interrelationship with the way the steps are performed. Therefore, it has been held that nonfunctional descriptive material will not distinguish the invention from prior art in term of patentability. (In re Gulack, 217 USPQ 401 (Fed. Cir. 1983), In re Ngai, 70 USPQ2d (Fed. Cir. 2004), In re Lowry, 32 USPQ2d 1031 (Fed. Cir. 1994); MPEP 2111.05).
Regarding claim 3, Portnoy discloses the workforce management may estimate the manpower required for the given functions or activities (see ¶ 184).
Portnoy and Hanley do not explicitly disclose the following limitations; however, Cella discloses the system of claim 2, wherein the summary analytic findings comprises an estimated Generative AI driven workforce capacity value and a Generative AI current state readiness (see ¶ 277-279, ¶ 564, ¶ 616, ¶ 732, ¶ 2495).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify the system of Portnoy and in view of Hanley to include the teaching of Cella in order to gain the commonly understood benefit of such adaption, such as providing the benefit by adapting new technologies to improve the accuracy of data processing. Since the combination of 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.
In addition, claim 3 merely describes the attributes of the summary analytic findings is directed to nonfunctional descriptive material because they cannot exhibit any functional interrelationship with the way the steps are performed. Therefore, it has been held that nonfunctional descriptive material will not distinguish the invention from prior art in term of patentability. (In re Gulack, 217 USPQ 401 (Fed. Cir. 1983), In re Ngai, 70 USPQ2d (Fed. Cir. 2004), In re Lowry, 32 USPQ2d 1031 (Fed. Cir. 1994); MPEP 2111.05).
Regarding claim 4, Portnoy and Hanley do not explicitly disclose the following limitations; however, Cella discloses the system of claim 3, wherein the Generative AI current state readiness comprises workers with foundational or adjacent skills (see ¶ 445, ¶ 2072); total headcount with AI related roles (see ¶ 2006, ¶ 2497-2498); and foundational or adjacent technologies (see ¶ 2259, ¶ 2557).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify the system of Portnoy and in view of Hanley to include the teaching of Cella in order to gain the commonly understood benefit of such adaption, such as providing the benefit by adapting new technologies to improve the accuracy of data processing. Since the combination of 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.
In addition, claim 4 merely describes the attributes of the Generative AI current state readiness is directed to nonfunctional descriptive material because they cannot exhibit any functional interrelationship with the way the steps are performed. Therefore, it has been held that nonfunctional descriptive material will not distinguish the invention from prior art in term of patentability. (In re Gulack, 217 USPQ 401 (Fed. Cir. 1983), In re Ngai, 70 USPQ2d (Fed. Cir. 2004), In re Lowry, 32 USPQ2d 1031 (Fed. Cir. 1994); MPEP 2111.05).
Regarding claim 5, Portnoy and Hanley do not explicitly disclose the following limitations; however, Cella discloses the system of claim 2, wherein the analytic details comprises an estimated value functional breakdown and peer benchmarking and a total Generative AI-driven workforce capacity value graphic (see ¶ 1216, ¶ 1219, ¶ 2600-2601).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify the system of Portnoy and in view of Hanley to include the teaching of Cella in order to gain the commonly understood benefit of such adaption, such as providing the benefit by adapting new technologies to improve the accuracy of data processing. Since the combination of 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.
In addition, claim 5 merely describes the attributes of the analytic details is directed to nonfunctional descriptive material because they cannot exhibit any functional interrelationship with the way the steps are performed. Therefore, it has been held that nonfunctional descriptive material will not distinguish the invention from prior art in term of patentability. (In re Gulack, 217 USPQ 401 (Fed. Cir. 1983), In re Ngai, 70 USPQ2d (Fed. Cir. 2004), In re Lowry, 32 USPQ2d 1031 (Fed. Cir. 1994); MPEP 2111.05).
Regarding claim 6, Portnoy and Hanley do not explicitly disclose the following limitations; however, Cella discloses the system of claim 5, wherein the total Generative AI-driven workforce capacity value graphic comprises top functions by value (see ¶ 2464-2465, ¶ 2471).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify the system of Portnoy and in view of Hanley to include the teaching of Cella in order to gain the commonly understood benefit of such adaption, such as providing the benefit by adapting new technologies to improve the accuracy of data processing. Since the combination of 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.
In addition, claim 6 merely describes the attributes of the total Generative AI-driven workforce capacity value graphic is directed to nonfunctional descriptive material because they cannot exhibit any functional interrelationship with the way the steps are performed. Therefore, it has been held that nonfunctional descriptive material will not distinguish the invention from prior art in term of patentability. (In re Gulack, 217 USPQ 401 (Fed. Cir. 1983), In re Ngai, 70 USPQ2d (Fed. Cir. 2004), In re Lowry, 32 USPQ2d 1031 (Fed. Cir. 1994); MPEP 2111.05).
Regarding claim 7, Portnoy and Hanley do not explicitly disclose the following limitations; however, Cella discloses the system of claim 6, wherein the top functions by value comprise supply chain opportunities (see ¶ 1827, ¶ 2734); sales opportunities (see ¶ 278, ¶ 338); and finance opportunities (see ¶ 1077, ¶ 2932).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify the system of Portnoy and in view of Hanley to include the teaching of Cella in order to gain the commonly understood benefit of such adaption, such as providing the benefit by adapting new technologies to improve the accuracy of data processing. Since the combination of 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.
In addition, claim 7 merely describes the attributes of the top functions by value is directed to nonfunctional descriptive material because they cannot exhibit any functional interrelationship with the way the steps are performed. Therefore, it has been held that nonfunctional descriptive material will not distinguish the invention from prior art in term of patentability. (In re Gulack, 217 USPQ 401 (Fed. Cir. 1983), In re Ngai, 70 USPQ2d (Fed. Cir. 2004), In re Lowry, 32 USPQ2d 1031 (Fed. Cir. 1994); MPEP 2111.05).
Regarding claim 8, Portnoy and Hanley do not explicitly disclose the following limitations; however, Cella discloses the system of claim 1, wherein the workforce capacity opportunity comprises technology readiness that includes: concentration by product category and modern technology framework alignment graphic (see ¶ 666, ¶ 1512, ¶ 2713).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify the system of Portnoy and in view of Hanley to include the teaching of Cella in order to gain the commonly understood benefit of such adaption, such as providing the benefit by adapting new technologies to improve the accuracy of data processing. Since the combination of 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.
In addition, claim 8 merely describes the attributes of the workforce capacity opportunity is directed to nonfunctional descriptive material because they cannot exhibit any functional interrelationship with the way the steps are performed. Therefore, it has been held that nonfunctional descriptive material will not distinguish the invention from prior art in term of patentability. (In re Gulack, 217 USPQ 401 (Fed. Cir. 1983), In re Ngai, 70 USPQ2d (Fed. Cir. 2004), In re Lowry, 32 USPQ2d 1031 (Fed. Cir. 1994); MPEP 2111.05).
Regarding claim 10, Portnoy discloses the system of claim 1, wherein the workforce capacity opportunity comprises a headcount ratio and an allocation of AI related roles (see ¶ 168-169, ¶ 180)
In addition, claim 10 merely describes the attributes of the workforce capacity opportunity is directed to nonfunctional descriptive material because they cannot exhibit any functional interrelationship with the way the steps are performed. Therefore, it has been held that nonfunctional descriptive material will not distinguish the invention from prior art in term of patentability. (In re Gulack, 217 USPQ 401 (Fed. Cir. 1983), In re Ngai, 70 USPQ2d (Fed. Cir. 2004), In re Lowry, 32 USPQ2d 1031 (Fed. Cir. 1994); MPEP 2111.05).
Regarding claim 11, Portnoy discloses a method for implementing a Generative AI Opportunity Assessment Tool by a computer processor coupled to one or more data sources that store and manage workforce data for at least one target entity (see ¶ 9, ¶ 49, ¶ 117) comprising:
identifying a target entity and a set of peer entities (see ¶ 56, ¶ 160-168);
generating a productivity estimate at an activity level based on workforce data relevant to the target entity and the human-to-machine coefficient (see Fig. 7(b); ¶ 87, ¶ 227, ¶ 239, ¶ 288);
determining an impact comprising a number of workers and total salaries (see Fig. 4(d); Fig. 6; ¶ 257-259);
generating a current state assessment of existing technology and skills within the target entity relating to Generative AI wherein the current state assessment is based on third party workforce salary and skills data at a role level (see Fig. 6; Fig. 7(j), pg. 2; ¶ 90-96, ¶ 121, ¶ 272-268, ¶ 293, ¶ 334);
applying the fine-tuned model to the target entity’s workforce data to determine an appropriate change management relating to technology and workforce shaping (see Fig. 6; Fig. 7(j), pg. 2; ¶ 90-96, ¶ 121, ¶ 272-268, ¶ 293, ¶ 334); and
identifying a workforce capacity opportunity for the target entity (see ¶ 49-54, ¶ 228-233, ¶ 332-334).
Portnoy does not explicitly disclose the following limitations; however, Hanley in an analogous art for human service management discloses
generating a baseline assessment, by applying an assessment model comprising the generative artificial intelligence model, of a financial opportunity for the target entity in leveraging Generative AI at scale (see ¶ 4, ¶ 54, ¶ 106, ¶ 121, claim 9).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify the system of Portnoy to include teaching of Hanley in order to gain the commonly understood benefit of such adaption, such as providing the benefit of enhancing computational efficiency, in turn of operational efficiency. Since the combination of 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.
Portnoy discloses the workforce management system may evaluate and validate managers’ and employees’ survey responses against current levels and against each other using artificial intelligence (AI) and natural language processing techniques (see ¶ 158-164); and the computer may compute the organizational structure based on alignment of the activities or competencies of two suborganizations of the group (see ¶ 10); and the workforce demographic analysis may access an organization’s health and key metrics include employee-contractor composition and ratios and influences with management organization develop strategy, attrition trend analysis…and alignment of employee’s goals with the organization’s mission (see ¶ 166).
Portnoy and Hanley do not explicitly disclose the following limitations; however, Cella in an analogous art for training artificial intelligence models discloses
providing a generative artificial intelligence model served through a model orchestration, the generative artificial intelligence model comprising an embedding and vector store, a feedback store, a fine tuning store, and model guardrails (see ¶ 2475, ¶ 2524, ¶ 2805, ¶ 2814); and
aligning organization role profiles of the target entity with a taxonomy that classifies work activities and processes (see ¶ 304-307, ¶ 409, ¶ 1367, ¶ 1755, ¶ 2773);
mapping workforce data of the target entity obtained from one or more third party data sources to the taxonomy (see ¶ 148, ¶ 412, ¶ 429, ¶ 631, ¶ 899, ¶ 1367);
fine-tuning the generative artificial intelligence model by integrating the workforce data of the target entity to generate a fine-tuned model that generates insights specific to the target entity (see ¶ 2489, ¶ 2531, ¶ 2536, ¶ 2542, ¶ 2549-2551, ¶ 2756); and
applying a unique set of use cases, identifying a set of organizational roles that illustrate Generative AI agents of change (see ¶ 28, ¶ 32-34, ¶ 732, ¶ 796, ¶ 2492, ¶ 2565).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify the system of Portnoy and in view of Hanley to include the teaching of Cella in order to gain the commonly understood benefit of such adaption, such as providing the benefit by adapting new technologies to improve the accuracy of data processing. Since the combination of 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.
Cella discloses a smart contract may split between the licensing athlete and split the funds according to a proportion of 60/20/20, whereby 60% of the funds for student, 20% of the funds for the athletic department, and 20% of the funds for the student athlete’s teammates (see ¶ 498); and accessing worker data including human resource database information including the salaries of various workers, information about the rates charged by service workers or other contractors (see ¶ 923), and inputting a set of data sources to the generative AI (see ¶ 2506).
Portnoy, Hanley and Cella do not explicitly disclose the following limitations; however, in an analogous art for processing production data discloses
determining, for each use case of the unique set of use cases, a human-to-machine coefficient associated with the use case, the human-to-machine coefficient representing, for an activity associated with the use case (see Abstract; pg. 4, ¶ 1-5; pg. 9, ¶ 4), a split between a machine proportion of the activity that is performable by the generative artificial intelligence model and a remaining human proportion of the activity (see pg. 10, ¶ 4; pg. 16, ¶ 6-8; pg. 25, claim 6);
generating a Generative AI driven workforce capacity value defined by role and function and supplemented with relevant use-cases by applying the machine proportion of the human-to-machine coefficient to the number of workers performing the activity and an average salary of the number of workers to compute a monetary opportunity and a corresponding percentage of time saved (see pg. 4, ¶ 3-5; pg. 8, ¶ 5-8; pg. 9, ¶ 1-4; pg. 15, ¶ 10-13; pg. 22, ¶ 1; pg. 23, ¶ 1, ¶ 10).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify the system of Portnoy and in view of Hanley and Cella to include the teaching of Liu in order to gain the commonly understood benefit of such adaption, such as providing the benefit of an additional layer of data analysis and resulting in a more focus solution. Since the combination of 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 12, Portnoy discloses the method of claim 11, wherein the workforce capacity opportunity comprises summary analytic findings and analytic details (see Fig. 3a-g; ¶ 53-54, ¶ 71, ¶ 254, ¶ 322, ¶ 339).
In addition, claim 12 merely describes the attributes of the workforce capacity opportunity is directed to nonfunctional descriptive material because they cannot exhibit any functional interrelationship with the way the steps are performed. Therefore, it has been held that nonfunctional descriptive material will not distinguish the invention from prior art in term of patentability. (In re Gulack, 217 USPQ 401 (Fed. Cir. 1983), In re Ngai, 70 USPQ2d (Fed. Cir. 2004), In re Lowry, 32 USPQ2d 1031 (Fed. Cir. 1994); MPEP 2111.05).
Regarding claim 13, Portnoy discloses the workforce management may estimate the manpower required for the given functions or activities (see ¶ 184).
Portnoy and Hanley do not explicitly disclose the following limitations; however, Cella discloses the method of claim 12, wherein the summary analytic findings comprises an estimated Generative AI driven workforce capacity value and a Generative AI current state readiness (see ¶ 277-279, ¶ 564, ¶ 616, ¶ 732, ¶ 2495).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify the system of Portnoy and in view of Hanley to include the teaching of Cella in order to gain the commonly understood benefit of such adaption, such as providing the benefit by adapting new technologies to improve the accuracy of data processing. Since the combination of 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.
In addition, claim 13 merely describes the attributes of the summary analytic findings is directed to nonfunctional descriptive material because they cannot exhibit any functional interrelationship with the way the steps are performed. Therefore, it has been held that nonfunctional descriptive material will not distinguish the invention from prior art in term of patentability. (In re Gulack, 217 USPQ 401 (Fed. Cir. 1983), In re Ngai, 70 USPQ2d (Fed. Cir. 2004), In re Lowry, 32 USPQ2d 1031 (Fed. Cir. 1994); MPEP 2111.05).
Regarding claim 14, Portnoy and Hanley do not explicitly disclose the following limitations; however, Cella discloses the method of claim 13, wherein the Generative AI current state readiness comprises workers with foundational or adjacent skills (see ¶ 445, ¶ 2072); total headcount with AI related roles (see ¶ 2006, ¶ 2497-2498); and foundational or adjacent technologies (see ¶ 2259, ¶ 2557).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify the system of Portnoy and in view of Hanley to include the teaching of Cella in order to gain the commonly understood benefit of such adaption, such as providing the benefit by adapting new technologies to improve the accuracy of data processing. Since the combination of 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.
In addition, claim 14 merely describes the attributes of the Generative AI current state readiness is directed to nonfunctional descriptive material because they cannot exhibit any functional interrelationship with the way the steps are performed. Therefore, it has been held that nonfunctional descriptive material will not distinguish the invention from prior art in term of patentability. (In re Gulack, 217 USPQ 401 (Fed. Cir. 1983), In re Ngai, 70 USPQ2d (Fed. Cir. 2004), In re Lowry, 32 USPQ2d 1031 (Fed. Cir. 1994); MPEP 2111.05).
Regarding claim 15, Portnoy and Hanley do not explicitly disclose the following limitations; however, Cella discloses the method of claim 12, wherein the analytic details comprises an estimated value functional breakdown and peer benchmarking and a total Generative AI-driven workforce capacity value graphic (see ¶ 1216, ¶ 1219, ¶ 2600-2601).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify the system of Portnoy and in view of Hanley to include the teaching of Cella in order to gain the commonly understood benefit of such adaption, such as providing the benefit by adapting new technologies to improve the accuracy of data processing. Since the combination of 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.
In addition, claim 15 merely describes the attributes of the analytic details is directed to nonfunctional descriptive material because they cannot exhibit any functional interrelationship with the way the steps are performed. Therefore, it has been held that nonfunctional descriptive material will not distinguish the invention from prior art in term of patentability. (In re Gulack, 217 USPQ 401 (Fed. Cir. 1983), In re Ngai, 70 USPQ2d (Fed. Cir. 2004), In re Lowry, 32 USPQ2d 1031 (Fed. Cir. 1994); MPEP 2111.05).
Regarding claim 16, Portnoy and Hanley do not explicitly disclose the following limitations; however, Cella discloses the method of claim 15, wherein the total Generative AI-driven workforce capacity value graphic comprises top functions by value (see ¶ 2464-2465, ¶ 2471).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify the system of Portnoy and in view of Hanley to include the teaching of Cella in order to gain the commonly understood benefit of such adaption, such as providing the benefit by adapting new technologies to improve the accuracy of data processing. Since the combination of 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.
In addition, claim 16 merely describes the attributes of the total Generative AI-driven workforce capacity value graphic is directed to nonfunctional descriptive material because they cannot exhibit any functional interrelationship with the way the steps are performed. Therefore, it has been held that nonfunctional descriptive material will not distinguish the invention from prior art in term of patentability. (In re Gulack, 217 USPQ 401 (Fed. Cir. 1983), In re Ngai, 70 USPQ2d (Fed. Cir. 2004), In re Lowry, 32 USPQ2d 1031 (Fed. Cir. 1994); MPEP 2111.05).
Regarding claim 17, Portnoy and Hanley do not explicitly disclose the following limitations; however, Cella discloses the method of claim 16, wherein the top functions by value comprise supply chain opportunities (see ¶ 1827, ¶ 2734); sales opportunities (see ¶ 278, ¶ 338); and finance opportunities (see ¶ 1077, ¶ 2932).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify the system of Portnoy and in view of Hanley to include the teaching of Cella in order to gain the commonly understood benefit of such adaption, such as providing the benefit by adapting new technologies to improve the accuracy of data processing. Since the combination of 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.
In addition, claim 17 merely describes the attributes of the top functions by value is directed to nonfunctional descriptive material because they cannot exhibit any functional interrelationship with the way the steps are performed. Therefore, it has been held that nonfunctional descriptive material will not distinguish the invention from prior art in term of patentability. (In re Gulack, 217 USPQ 401 (Fed. Cir. 1983), In re Ngai, 70 USPQ2d (Fed. Cir. 2004), In re Lowry, 32 USPQ2d 1031 (Fed. Cir. 1994); MPEP 2111.05).
Regarding claim 18, Portnoy and Hanley do not explicitly disclose the following limitations; however, Cella discloses the method of claim 11, wherein the workforce capacity opportunity comprises technology readiness that includes: concentration by product category and modern technology framework alignment graphic (see ¶ 666, ¶ 1512, ¶ 2713).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify the system of Portnoy and in view of Hanley to include the teaching of Cella in order to gain the commonly understood benefit of such adaption, such as providing the benefit by adapting new technologies to improve the accuracy of data processing. Since the combination of 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.
In addition, claim 18 merely describes the attributes of the workforce capacity opportunity is directed to nonfunctional descriptive material because they cannot exhibit any functional interrelationship with the way the steps are performed. Therefore, it has been held that nonfunctional descriptive material will not distinguish the invention from prior art in term of patentability. (In re Gulack, 217 USPQ 401 (Fed. Cir. 1983), In re Ngai, 70 USPQ2d (Fed. Cir. 2004), In re Lowry, 32 USPQ2d 1031 (Fed. Cir. 1994); MPEP 2111.05).
Regarding claim 19, Portnoy and Hanley do not explicitly disclose the following limitations; however, Cella discloses the method of claim 18, wherein the product category comprises: automation, environment, intelligence, interaction, information and cyber (see ¶ 307, ¶ 900, ¶ 2710).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify the system of Portnoy and in view of Hanley to include the teaching of Cella in order to gain the commonly understood benefit of such adaption, such as providing the benefit by adapting new technologies to improve the accuracy of data processing. Since the combination of 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.
In addition, claim 19 merely describes the attributes of the product category is directed to nonfunctional descriptive material because they cannot exhibit any functional interrelationship with the way the steps are performed. Therefore, it has been held that nonfunctional descriptive material will not distinguish the invention from prior art in term of patentability. (In re Gulack, 217 USPQ 401 (Fed. Cir. 1983), In re Ngai, 70 USPQ2d (Fed. Cir. 2004), In re Lowry, 32 USPQ2d 1031 (Fed. Cir. 1994); MPEP 2111.05).
Regarding claim 20, Portnoy discloses the method of claim 11, wherein the workforce capacity opportunity comprises a headcount ratio and an allocation of AI related roles (see ¶ 168-169, ¶ 180)
In addition, claim 10 merely describes the attributes of the workforce capacity opportunity is directed to nonfunctional descriptive material because they cannot exhibit any functional interrelationship with the way the steps are performed. Therefore, it has been held that nonfunctional descriptive material will not distinguish the invention from prior art in term of patentability. (In re Gulack, 217 USPQ 401 (Fed. Cir. 1983), In re Ngai, 70 USPQ2d (Fed. Cir. 2004), In re Lowry, 32 USPQ2d 1031 (Fed. Cir. 1994); MPEP 2111.05).
Regarding claim 21, Portnoy discloses a non-transitory computer-readable medium containing program instructions that, when executed by a computer processor coupled to one or more data sources and manage workforce data for at least one target entity, cause the computer processor to perform a method for implementing a Generative AI Opportunity Assessment Tool, the method comprising:
generating a productivity estimate at an activity level based on workforce data relevant to the target entity and the human-to-machine coefficient (see Fig. 7(b); ¶ 87, ¶ 227, ¶ 239, ¶ 288);
determining an impact comprising a number of workers and total salaries (see Fig. 4(d); Fig. 6; ¶ 257-259);
generating a current state assessment of existing technology and skills within the target entity relating to Generative AI wherein the current state assessment is based on third party workforce salary and skills data at a role level (see Fig. 6; Fig. 7(j), pg. 2; ¶ 90-96, ¶ 121, ¶ 272-268, ¶ 293, ¶ 334);
applying the fine-tuned model to the target entity’s workforce data to determine an appropriate change management relating to technology and workforce shaping (see Fig. 6; Fig. 7(j), pg. 2; ¶ 90-96, ¶ 121, ¶ 272-268, ¶ 293, ¶ 334); and
identifying a workforce capacity opportunity for the target entity (see ¶ 49-54, ¶ 228-233, ¶ 332-334).
Portnoy does not explicitly disclose the following limitations; however, Hanley in an analogous art for human service management discloses
generating a baseline assessment, by applying an assessment model comprising the generative artificial intelligence model, of a financial opportunity for the target entity in leveraging Generative AI at scale (see ¶ 4, ¶ 54, ¶ 106, ¶ 121, claim 9).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify the system of Portnoy to include teaching of Hanley in order to gain the commonly understood benefit of such adaption, such as providing the benefit of enhancing computational efficiency, in turn of operational efficiency. Since the combination of 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.
Portnoy discloses the workforce management system may evaluate and validate managers’ and employees’ survey responses against current levels and against each other using artificial intelligence (AI) and natural language processing techniques (see ¶ 158-164); and the computer may compute the organizational structure based on alignment of the activities or competencies of two suborganizations of the group (see ¶ 10); and the workforce demographic analysis may access an organization’s health and key metrics include employee-contractor composition and ratios and influences with management organization develop strategy, attrition trend analysis…and alignment of employee’s goals with the organization’s mission (see ¶ 166).
Portnoy and Hanley do not explicitly disclose the following limitations; however, Cella in an analogous art for training artificial intelligence models discloses
providing a generative artificial intelligence model served through a model orchestration, the generative artificial intelligence model comprising an embedding and vector store, a feedback store, a fine tuning store, and model guardrails (see ¶ 2475, ¶ 2524, ¶ 2805, ¶ 2814); and
aligning organization role profiles of the target entity with a taxonomy that classifies work activities and processes (see ¶ 304-307, ¶ 409, ¶ 1367, ¶ 1755, ¶ 2773);
mapping workforce data of the target entity obtained from one or more third party data sources to the taxonomy (see ¶ 148, ¶ 412, ¶ 429, ¶ 631, ¶ 899, ¶ 1367);
fine-tuning the generative artificial intelligence model by integrating the workforce data of the target entity to generate a fine-tuned model that generates insights specific to the target entity (see ¶ 2489, ¶ 2531, ¶ 2536, ¶ 2542, ¶ 2549-2551, ¶ 2756); and
applying a unique set of use cases, identifying a set of organizational roles that illustrate Generative AI agents of change (see ¶ 28, ¶ 32-34, ¶ 732, ¶ 796, ¶ 2492, ¶ 2565).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify the system of Portnoy and in view of Hanley to include the teaching of Cella in order to gain the commonly understood benefit of such adaption, such as providing the benefit by adapting new technologies to improve the accuracy of data processing. Since the combination of 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.
Cella discloses a smart contract may split between the licensing athlete and split the funds according to a proportion of 60/20/20, whereby 60% of the funds for student, 20% of the funds for the athletic department, and 20% of the funds for the student athlete’s teammates (see ¶ 498); and accessing worker data including human resource database information including the salaries of various workers, information about the rates charged by service workers or other contractors (see ¶ 923), and inputting a set of data sources to the generative AI (see ¶ 2506).
Portnoy, Hanley and Cella do not explicitly disclose the following limitations; however, in an analogous art for processing production data discloses
determining, for each use case of the unique set of use cases, a human-to-machine coefficient associated with the use case, the human-to-machine coefficient representing, for an activity associated with the use case (see Abstract; pg. 4, ¶ 1-5; pg. 9, ¶ 4), a split between a machine proportion of the activity that is performable by the generative artificial intelligence model and a remaining human proportion of the activity (see pg. 10, ¶ 4; pg. 16, ¶ 6-8; pg. 25, claim 6);
generating a Generative AI driven workforce capacity value defined by role and function and supplemented with relevant use-cases by applying the machine proportion of the human-to-machine coefficient to the number of workers performing the activity and an average salary of the number of workers to compute a monetary opportunity and a corresponding percentage of time saved (see pg. 4, ¶ 3-5; pg. 8, ¶ 5-8; pg. 9, ¶ 1-4; pg. 15, ¶ 10-13; pg. 22, ¶ 1; pg. 23, ¶ 1, ¶ 10).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify the system of Portnoy and in view of Hanley and Cella to include the teaching of Liu in order to gain the commonly understood benefit of such adaption, such as providing the benefit of an additional layer of data analysis and resulting in a more focus solution. Since the combination of 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.
Xu et al., (CN 110956371 A) discloses a method for optimizing green schedules of smart manufacturing workshop facing complicated man-machine coupling.
Barnes et al., (WO 2007009034 A2) discloses a system for integrating functions of human capital resource management pursuant to workforce development and maintenance
Chishti (WO 2018122614 A2) discloses techniques for L3 paring and workforce management in a contact center system comprising identifying a plurality of contacts waiting for assignment, identifying a plurality of agents available for assignment, and selecting at least one of the plurality of agents with at least one of the plurality of contacts for assignment and connection within the contact center system.
Benson et al., (WO 2022123318 A1) discloses an enterprise workforce management platform for managing workforce and creating workflow for one of a plurality of entities associated with the enterprise.
Budhwar et al., “Human resource management in the age of generative artificial intelligence: Perspectives and research direction on ChatGPT”, Human Resource Management Journal Published by John Wiley & Sons Ltd., 2023.
Park et al., “Generative Agents: Interactive Simulacra of Human Behavior”, Stanford University, Stanford, USA, UIST ’23, October 29 - November 1, 2023.
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.
Any inquiry concerning this communication or earlier communications from the examiner should be directed to PAN CHOY whose telephone number is (571)270-7038. The examiner can normally be reached 5/4/9 compressed work schedule.
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/PAN G CHOY/Primary Examiner, Art Unit 3624
