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 .
Status of Claims
This Final action is in reply to applicant’s arguments/amendments filed 11/24/2025.
Claims 5, 20 and 27 are cancelled.
Claims 9-15 were previously cancelled.
Claims 1, 6-8, 16 and 21-24 have been amended.
Claims 1-4, 6-8, 16-19 and 21-26 are pending.
Response to Arguments/Amendments
With respect to the 35USC 112(a) rejection, applicant’s amendments and arguments have been considered, however the amendments do not overcome the rejection for the independent claims. Applicant generally states that the claims have been amended for clarity, however Examiner notes that the claims were rejected for failing to comply with the written description requirement. Although applicant has amended the claims, applicant failed/fails to identify where the claim language is supported in the disclosure. The claim defines the invention in functional language specifying a desired result but the disclosure fails to sufficiently identify how the function is performed or the result is achieved. Applicant’s specification only generically describes using data/information to estimate (predict) an outcome, but fails to provide the requisite rules, steps, algorithm, structure in the disclosure for how the estimates, weights, and scores are determined (i.e. respective weight(s), and the mathematical formula/equation/calculation(s), and/or rules) which are essential for the limitation(s). Applicant’s failure to disclose any meaningful processes with the claimed recited subject matter raises questions as to whether applicant truly had possession of these features at the time of filing. Therefore, the rejection is maintained. The respective dependent claims do not remedy this flaw, hence the rejection for the dependent claims is also maintained.
With respect to the 35USC 112(a) rejection and claim 8, applicant’s amendments overcome the rejection; it has been withdrawn.
As it relates to the 35USC 112(b) rejection, applicant merely states that the claims have been amended for clarity and requests that the rejection be withdrawn. Applicant’s arguments have been considered but are unpersuasive. The amendments do not overcome the rejection for the independent claims; therefore, the rejection is maintained. Applicant again fails to clearly identify and provide the requisite steps for performing the claimed functions; hence the Examiner is unable to determine the metes and bounds or interpret the exact scope of the claim limitations (i.e. how is/are the weight(s) determined for the respective output(s)/estimate(s) and subsequently used to generate a score for each of the subset of third-party service providers). The respective dependent claims do not remedy this flaw, hence the rejection for the dependent claims is also maintained.
With respect to the 35USC 112(b) rejection and claim 8, applicant’s amendments overcome the rejection; it has been withdrawn.
With respect to the 35 USC 101 rejection, applicant simply disagrees and provides a general allegation that the amended claims are directed to a practical application. Applicant’s arguments have been considered but are unpersuasive. Although the claims have been amended, Examiner maintains that the claims are directed to the abstract idea for identifying a subset of third-party service providers that are a potential match to provide services to a first client, obtaining a first client cluster that corresponds to the first client, obtaining a first estimate identifying a first weight associated with the first client, obtaining a second estimate, identifying a second weight of the first client, and generating a score for each subset of third-party service providers using the first weight and second weight in a ranking model and providing a notification indicating the scores for the subset of third-party service providers in a computing environment. The claimed invention is merely using generic computing components (“software as a service (SaaS) management platform”, “first trained artificial intelligence (AI) model”, “second trained AI model”, “third trained AI model”, “a processing device”, “ranking model”, [claim 1], “memory’, “one or more processing devices” [claim 16], “non-transitory computer-readable storage medium [claim 24]), as a tool for data gathering/analysis and manipulation to perform and automate the abstract idea. There is no improvement to the “software as a service (SaaS) management platform”, “first trained artificial intelligence (AI) model”, “second trained AI model”, “third trained AI model”, “a processing device”, “ranking model”, [claim 1], “memory’, “one or more processing devices” [claim 16], “non-transitory computer-readable storage medium [claim 24], nor is there evidence in the disclosure to suggest achieving an actual improvement in the computer functionality itself. Further, the claimed invention includes recitations for computing components used in a manner that is well-understood, routine, provide conventional activities previously known in the art, and recited at a high level of generality (see applicant’s disclosure, ¶8-¶11, ¶22, ¶77, ¶88, ¶363, ¶365, ¶370). The dependent claims further recite the abstract idea without imposing any meaningful limits on practicing the abstract idea since they merely recite additional data gathering and processing steps (identifying, providing, performing, obtaining) which are no more than mere instructions to apply the exception using a computer or with computing components. Therefore, the dependent claims fail to cure the deficiencies of the independent claim from which they depend and are rejected under the same grounds.
Accordingly, even when considering the claims both individually and as an ordered combination, they fail to add subject matter beyond the judicial exception that is not well-understood, routine and conventional in the field. Therefore, applicant has not shown an improvement in the computer functionality itself, or any specific computer technology; nor do the claims integrate the abstract idea into any practical application under the guidance of MPEP section 2106.04(d) or 2106.05(a). In view of the above, Examiner maintains that the claimed invention is directed to an abstract idea.
Regarding the 35USC 103 rejection, applicant argues the amended claim limitations which were not previously presented, nor applied against the prior art. Applicant’s amendments necessitated new grounds of rejection, therefore the arguments are moot. Examiner has modified the rejection based on applicant’s amendments to further explain how the limitations are being interpreted and has addressed each of the claims as noted below in this Final action.
Claim Rejections - 35 USC § 112
The following is a quotation of the first paragraph 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 the first paragraph of pre-AIA 35 U.S.C. 112:
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 1-4, 6-8, 16-19 and 21-26 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 claim(s) contains subject matter which was 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 applications subject to pre-AIA 35 U.S.C. 112, the inventor(s), at the time the application was filed, had possession of the claimed invention.
Claims 1, 16 and 24 recite in part,
“obtaining, from a second trained Al model, a second output indicating a first estimate… wherein the first estimate identifies a first weight that reflects whether the response from the first third-party service provider will satisfy second client preferences of the first client cluster” and
“obtaining, from a third trained Al model, a third output indicating a second estimate identifying a second weight that reflects an estimate of occurrences of one or more future life events for a plurality of employees of the first client…” Examiner was unable to find support in the specification for the limitations, nor does the specification demonstrate that applicant has made an invention that achieves the claimed function since the invention is not described in sufficient details such that one of ordinary skill in the art can reasonably conclude that the inventor had possession of the claimed invention. For example, the specification as filed does not properly disclose any rules logic, steps, algorithms, mathematical formulas, calculations, equations for how” a first estimate identifying a first weight, a second estimate identifying a second weight” is/are determined and subsequently used for generating a score for each of the subset of third-party service providers whereby the score indicates a likelihood that a respective third-party service provider is suitable for the first client. Applicant’s specification fails to identify how the respective (weight) criteria (i.e. value/number/integer/level?) for each estimate is determined and used/weighted by the ranking model to generate a score for each of the subset of third-party service providers whereby the score indicates a likelihood that a respective third-party service provider is suitable for the first client. The limitations recite functions (e.g., obtaining from a second trained (AI) model, a second output indicating a first estimate…wherein the first estimate identifies a first weight; obtaining from a third trained AI model, a third output indicating a second estimate identifying a second weight) with a desired result (i.e. one or more outputs identifying a respective weight), however applicant’s disclosure, does not provide sufficient written description support for the recited “trained” AI model(s) necessary to achieve the recited results. To satisfy the written description requirement of 35 U.S.C. § 112, first paragraph, the specification must reasonably convey to an artisan of ordinary skill that Appellant had possession of the claimed invention at the time the application was filed. Vasudevan Software, Inc. v. MicroStrategy, Inc., 782 F.3d 671, 682 (Fed. Cir. 2015) (citing Ariad Pharm., Inc. v. Eli Lilly & Co., 598 F.3d 1336, 1351 (Fed. Cir. 2010) (en banc)). Functional claim language that merely describes an intended result and fails to support the scope of the claimed invention is insufficient to show possession, even when the claim recitations are found word-for-word in the specification. Vasudevan, 782 F.3d at 682 ("[t]he written description requirement is not met if the specification merely describes a 'desired result"') (citing Ariad, 598 F.3d at 1349); Enzo Biochem, Inc. v. Gen-Probe, Inc., 323 F.3d 956, 968 (Fed. Cir. 2002) ("[t]he appearance of mere indistinct words in a specification or a claim, even an original claim, does not necessarily satisfy" the written description requirement). The specification must explain, for example, how Appellant intended to achieve the claimed function to satisfy the written description requirement. Vasudevan, 782 F.3d at 683. While "[t]here is no rigid requirement that the disclosure contain 'either examples or an actual reduction to practice,"' due to the written description requirement, the Specification must set forth "an adequate description that 'in a definite way identifies the claimed invention' in sufficient detail such that a person of ordinary skill would understand that the inventor had made the invention at the time of filing." Allergan, Inc. v. Sandoz Inc., 796 F.3d 1293, 1308 (Fed. Cir. 2015) (citing Ariad, 598 F.3d at 1352); see also Examining Computer-Implemented Functional Claim Limitations for Compliance with 35 US.C. 112, 84 F.R. 57, 61- 62 (January 7, 2019) ("112 Guidance"). Here, the specification does not sufficiently support the recited functions and or their use to achieve the recited result in the aforementioned limitations; nor has applicant demonstrated they possessed a second or third “trained AI model” capable of indicating/identifying the outputs as claimed from the recited inputs as claimed. The most relevant discussion in applicant’s specification generically occurs in ¶49: “a first AI model can generate an output (e.g., consumer usage weight) that estimates usage by a consumer of services provided by a producer (e.g., estimates the amount of services the consumer will consume and/or the type of services the consumer will consume”. The disclosure at ¶84-¶87 appears to be a general overview discussing generic artificial intelligence models. However, the specification fails to provide the requisite structure, technique, method, algorithm, rules logic regarding what a “trained AI model” must encompass such that the indicating/identifying may be accomplished because no particular “trained AI model” is provided in applicant’s disclosure. This is akin to claiming a computer performs the claimed functionality without reciting the necessary steps the computer must take to achieve the results and without reciting necessary algorithms which the computer must perform to achieve the claimed results.
Instead, applicant’s specification at best describes generic types of AI models which may be used, see specification at ¶84-¶87. However, general acknowledgement of entire classes or fields of AI models is not evidence that applicant is in possession of a particular “trained AI model” which is capable of generating the “output” now claimed from the “input” which is claimed to be provided to the generically recited “trained” AI model(s).
Under these circumstances, the Examiner has determined the specification merely states a wish for the functions and desired result recited in the aforementioned limitations, and does not demonstrate how the applicant actually intended to achieve the claimed functions and result. Further, the specification does not describe the claimed invention in sufficient detail such that an ordinarily skilled artisan would understand that the inventor had made the invention at the time of filing. Thus, the Examiner rejects claims 1, 16 and 24 under 35 U.S.C. § 112(a) for a lack of written description support. The respective dependent claims do not remedy this flaw; therefore, they are also rejected as failing to comply with the written description requirement.
Claims 1, 16 and 24 recite in part, “generating by a processing device a score for each of the subset of third-party service providers using the first weight of the first estimate and the second weight of the second estimate as parameters in a ranking model”. Examiner was not able to find support in the disclosure that describes the claimed limitation so that one skilled in the art can recognize what is claimed. The appearance of mere indistinct words in a specification or a claim, even an original claim, does not necessarily satisfy that requirement. The most relevant discussion in applicant’s specification generically occurs in ¶49: “a first AI model can generate an output (e.g., consumer usage weight) that estimates usage by a consumer of services provided by a producer (e.g., estimates the amount of services the consumer will consume and/or the type of services the consumer will consume”; ¶102: “A ranking model 210 can use the model inputs 201 to produce producer match data (e.g., model outputs 202). The producer match data can include a ranked list of a subset of producers 212. The ranked list can be ranked on scores that indicate a certain producer is a match for the consumer”; ¶107: “each producer of the ranked subset of producers can be associated with a particular score determined by the ranking model 210”; ¶109: “The score can indicate a degree to which a producer and consumer match”. However, it does not adequately describe how a score for each of the subset of third-party service providers using the first weight…and the second weight… (as parameters in a ranking model) is determined, nor does it proved the requisite criteria for how the weights are determined (i.e. a value/number/level?) and used as parameters in a ranking model. Further, applicant has not pointed out where the new (amended) claim language is supported in the disclosure as filed. The claim defines the invention in functional language specifying a desired result but the disclosure fails to sufficiently identify how the function is performed or the result is achieved. Applicant’s specification only generically describes using data/information (a response from a first third-party service provider satisfying client preferences) to estimate (predict) an outcome, but fails to provide the requisite rules, steps, algorithm, structure in the disclosure for how applicant’s score for each of the subset of third-party service providers using the first weight of the first estimate and the second weight of the second estimate is determined using the respective weights as parameters in a ranking model. The specification does not provide the requisite rules, steps, algorithm, (i.e. respective weight(s), criteria, values, mathematical formula/equation/calculation(s)) to achieve the recited result in the aforementioned limitation which are essential for this limitation. Applicant’s failure to disclose any meaningful processes with the above recited subject matter raises questions as to whether applicant truly had possession of these features at the time of filing.
Under these circumstances, the Examiner has determined the specification merely states a wish for the functions and desired result recited in the aforementioned limitation, and does not demonstrate how the applicant actually intended to achieve the claimed functions and result. Further, the specification does not describe the claimed invention in sufficient detail such that an ordinarily skilled artisan would understand that the inventor had made the invention at the time of filing. Thus, the Examiner rejects claims 1, 16 and 24 under 35 U.S.C. § 112(a) for a lack of written description support. The respective dependent claims do not remedy this flaw; therefore, they are also rejected as failing to comply with the written description requirement.
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 1-4, 6-8, 16-19 and 21-26 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 the inventor or a joint inventor (or for applications subject to pre-AIA 35 U.S.C. 112, the applicant), regards as the invention.
Claims 1, 16 and 24 recite in part, “the score indicating a likelihood that a respective third-party service provider of the subset of third-party service providers is suitable for the first client”. The term “suitable” is a relative term which renders the claim indefinite. Further, it is not defined by the claim, nor does the specification provide a standard for ascertaining the requisite degree, and one of ordinary skill in the art would not be reasonably apprised of the scope of the invention. Although the specification describes determining a match using scores and describes the matches as suitable, there is no degree or level of suitability described that defines or limits what qualifies as suitable for matching purposes. The respective dependent claims do not remedy this flaw; therefore, they are also rejected. Appropriate clarification 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 1-4, 6-8, 16-19 and 21-26 are rejected under 35 U.S.C. § 101 because the claimed invention is directed to an abstract idea without significantly more. Claims 1-4, 6-8, 16-19 and 21-26 are directed to a system, process (method) and non-transitory computer readable storage medium. Thus, each of the claims fall within one of the four statutory categories.
Step 2A-Prong 1: Claim 1 recites in part, “identifying, among a plurality of third-party service providers, a subset of third-party service providers that are a potential match to provide services, to a first client, wherein the services provided by the subset of third-party service providers are facilitated via a software-as-a-service (SaaS) management platform,
“obtaining, from a first trained artificial intelligence (Al) model, a first output indicating a client cluster identifier identifying, among a plurality of consumer clusters, a first client cluster that corresponds to the first client; wherein clients identified by the first client cluster are grouped based on one or more characteristics shared with the first client;
“obtaining, from a second trained AI model, a second output indicating a first estimate that a response from a first third-party service provider of the subset of third-party service providers to a request for services for the first client cluster will satisfy first client preferences associated with the first client; wherein the first client preferences identify a type of services requested, wherein the first estimate identifies a first weight that reflects whether the response from the first third-party service provider will satisfy second client preferences of the first client cluster;
“obtaining from a third trained AI model, a third output indicating a second estimate identifying a second weight that reflects an estimate of occurrences of one or more future life events for a plurality of employees of the first client;
“generating by the processing device a score for each of the subset of third-party service providers using the first weight of the first estimate and the second weight of the second estimate as parameters in a ranking model, the score indicating a likelihood that a respective third-party service provider of the subset of third-party providers is suitable for the first client;
“providing a notification indicating the scores for the subset of third-party service providers....”
The underlined limitations above demonstrate independent claim 1 is directed toward the abstract idea for identifying a subset of third-party service providers that are a potential match to provide services to a first client, obtaining a first client cluster that corresponds to the first client, obtaining a first estimate identifying a first weight associated with the first client, obtaining a second estimate, identifying a second weight of the first client, and generating a score for each subset of third-party service providers using the first weight and second weight in a ranking model and providing a notification indicating the scores for the subset of third-party service providers in a computing environment. Applicant’s specification emphasizes a computer implemented method/system related to matching a producer and consumer, obtaining a consumer cluster identifier, generating an estimate that a response from a first producer will satisfy consumer preferences associated with the first consumer cluster and generating a score indicating a likelihood that a respective producer is a match for the first consumer and providing a notification. Applicant’s specification also discusses that a “consumer” or a “client organization” (also referred to as ‘client’ herein) can refer to an entity that accesses services from a platform, such as the SaaS management platform provided by a first-party organization (agent), and that, the consumer can subscribe to third-party products and/or services provided by a third-party, such as a producer. In some embodiments, the consumer accesses or consumes products and/or services of a producer (e.g., third-party product and/or services) via the SaaS management platform where the products and/or services are facilitated by the SaaS management platform. (¶1, 4, ¶56).
Representative Claim 1 is considered an abstract idea because the underlined limitations as claimed pertains to (i) commercial or legal interactions (including agreements in the form of contracts; legal obligations; advertising, marketing or sales activities or behaviors; business relations) “identifying, among a plurality of third-party service providers, a subset of third-party service providers that are a potential match to provide services; generating…a score for each of the subset of third-party service providers …. indicating a likelihood that a respective third-party service provider …is suitable for the first client”; and (ii) managing personal behavior or relationships or interactions between people (including social activities, teaching, and following rules or instructions) whereby identifying and matching potential third-party service providers to a client are facilitated via a SaaS management platform; a first weight, second weight associated with the first client are obtained and a score is generated using the weights in a ranking model and providing a notification indicating a likelihood that a respective third-party provider is a match for the first client are directed to managing interactions between people and following rules or instructions; hence the claimed invention is directed to the certain methods of organizing human activity groupings of abstract ideas. Hence, the claim recites an abstract idea--see MPEP 2106.04(II).
Step 2A-Prong 2: This judicial exception is not integrated into a practical application because the additional elements “software as a service (SaaS) management platform”, “first trained artificial intelligence (AI) model”, “second trained AI model”, “third trained AI model”, “a processing device”, “ranking model”, [claim 1], “memory’, “one or more processing devices” [claim 16], “non-transitory computer-readable storage medium [claim 24], for (identifying, providing, performing, obtaining) data gathering and analysis merely provide instructions for identifying and matching potential third-party service providers to a client obtaining a first weight, second weight associated with the first client, generating a score using the weights in a ranking model and providing a notification indicating a likelihood that a respective third-party provider is a match for the first client and implement the abstract idea recited above utilizing the “software as a service (SaaS) management platform”, “first trained artificial intelligence (AI) model”, “second trained AI model”, “third trained AI model”, “a processing device”, “ranking model”, [claim 1], “memory’, “one or more processing devices” [claim 16], “non-transitory computer-readable storage medium [claim 24], as tools to perform the abstract idea, and generally links the abstract idea to a particular technological environment. See MPEP 2106.05 (f-h).
Independent claim 1 fails to operate the recited “software as a service (SaaS) management platform”, “first trained artificial intelligence (AI) model”, “second trained AI model”, “third trained AI model”, “a processing device”, “ranking model”, [claim 1], “memory’, “one or more processing devices” [claim 16], “non-transitory computer-readable storage medium [claim 24] (which is/are merely a nominal recitation of a standard computer technology, database and hardware/software components) in any exceptional manner, and there is no evidence in the disclosure to suggest achieving an actual improvement in the computer functionality itself, or improvement in any specific computer technology other than utilizing ordinary computational tools to automate and perform the abstract idea for identifying a subset of third-party service providers that are a potential match to provide services to a first client, obtaining a first client cluster that corresponds to the first client, obtaining a first estimate identifying a first weight associated with the first client, obtaining a second estimate, identifying a second weight of the first client, and generating a score for each subset of third-party service providers using the first weight and second weight in a ranking model and providing a notification indicating the scores for the subset of third-party service providers in a computing environment —see MPEP 2106.05(a). Accordingly, applicant has not shown an improvement or practical application under the guidance of MPEP section 2106.04(d) or 2106.05(a). Applicant’s limitations as recited above do nothing more than supplement the abstract using generic computer and networking components performing generic computer functions (identifying, providing, performing, obtaining) such that it amounts to no more than mere instruction to apply the exception using a generic computer component-see MPEP 2106.05(f) and linking the use of the judicial exception to a particular technological environment or field of use as discussed in MPEP 2106.05(h). Independent claims 16 and 24 recite substantially similar limitations as independent claim 1, therefore they also recite the same abstract idea.
Dependent claims 2-4, 6-8, 17-19, 21-23, 25 and 26 fail to cure the deficiencies of the above noted independent claim from which they depend and are therefore rejected under the same grounds. The dependent claims further recite the abstract idea without imposing any meaningful limits on practicing the abstract idea. Dependent claims 2-4, 6-8, 17-19, 21-23, 25 and 26 recite additional data gathering and processing steps (identifying, performing, generating, obtaining, providing). For example dependent claims 2, 17 and 25 recite in part, “wherein identifying, among the plurality of producers, the subset of producers is based on”; claims 3, 18 and 26 recite in part, “providing a first input to the first trained AI model”, claims 4 and 19 recite in part, “preforming a pre-processing operation”; claims 6 and 21 recite in part, “providing a second input to the second trained AI model”; claims 7 and 22 recites in part, “obtaining, from the third trained AI model”; claims 8 and 23 recite in part, “wherein the one or more future life events comprises”, which is still directed toward the abstract idea identified previously and are no more than mere instructions to apply the exception using a computer or with computing components. Therefore, the abstract idea fails to integrate into any practical application. Thus, under Step 2A-Prong Two the claims are directed to an abstract idea.
Step 2B: The claim(s) does/do not include additional elements that are sufficient to amount to significantly more than the judicial exception because as discussed above, with respect to integration of the abstract idea into a practical application, and giving the broadest reasonable interpretation of the claim limitations in light of the specification, the “software as a service (SaaS) management platform”, “first trained artificial intelligence (AI) model”, “second trained AI model”, “third trained AI model”, “a processing device”, “ranking model”, [claim 1], “memory’, “one or more processing devices” [claim 16], “non-transitory computer-readable storage medium [claim 24], amounts to no more than mere instructions to apply the exception using a generic computer components and linking the use of the judicial exception to a computing environment (see applicant’s disclosure, ¶49: “a first AI model can generate an output (e.g., consumer usage weight) that estimates usage by a consumer of services provided by a producer (e.g., estimates the amount of services the consumer will consume and/or the type of services the consumer will consume”; ¶77: “the SaaS management platform 120 may include one or more computing devices (such as a rackmount server, a router computer, a server computer, a personal computer, a mainframe computer, a laptop computer, a tablet computer, a desktop computer, etc.), data stores (e.g., hard disks, memories, databases), networks, software components, or hardware components that can be used to provide a user with access to data or services”; ¶84: “an artificial intelligence (AI) model (e.g., also referred to as an “machine learning model” herein) can include a discriminative AI model (also referred to as “discriminative machine learning model” herein), a generative AI model (also referred to as “generative machine learning model” herein), and/or other AI model”; ¶363: “The processing device 802 represents one or more general-purpose processing devices such as a microprocessor, central processing unit, or the like”; ¶365: “The sets of instructions of the system 100 and of training set generator 131 or of the benefits module 151 can also reside, completely or at least partially, within the main memory 804 and/or within the processing device 802 during execution thereof by the system 800, the main memory 804 and the processing device 802 also constituting computer-readable storage media”; ¶369: “it is appreciated that throughout the description, discussions utilizing terms such as “generating”, “providing”, “obtaining”, “identifying”, “determining”, or the like, refer to the actions and processes of a computer system, or similar electronic computing device, that manipulates and transforms data represented as physical (e.g., electronic) quantities within the computer system memories or registers into other data similarly represented as physical quantities within the computer system memories or registers or other such information storage, transmission or display devices”; ¶370: “The disclosure also relates to an apparatus for performing the operations herein. This apparatus can be specially constructed for the required purposes, or it can include a general-purpose computer selectively activated or reconfigured by a computer program stored in the computer. Such a computer program can be stored in a computer readable storage medium”; ¶371: “The terms, “first”, “second”, “third,”, “fourth”, etc as used herein are meant as labels to distinguish among different elements”)
Further, giving the broadest reasonable interpretation of applicant’s first trained artificial intelligence (AI) model, second trained AI model; third trained AI model [claims 1, 16 and 24]; and fourth trained AI model [dependent claims 7 and 22] in light of the specification, the claimed element(s) are generically used to further process and transmit received data (see ¶8: “obtaining, from a second trained AI model, a second output indicating the estimate that the response from the first producer of the subset of producers to the request for services for the first consumer cluster will satisfy consumer preferences associated with the first consumer cluster”; ¶9: “providing a second input to the second trained AI model”; ¶10: “obtaining, from a third trained artificial intelligence (AI) model, a third output”; ¶11: “obtaining, from a fourth trained AI model, a fourth output”; ¶22: “using trained AI models to identify producer match data”) hence, the first trained artificial intelligence (AI) model, second trained AI model; third trained AI model [claims 1, 16 and 24]; fourth trained AI model [dependent claims 7 and 22] are merely used in a manner that is well-understood, routine and conventional in the field. The additional elements amount to no more than applying the judicial exception using generic computing components, and linking the use of the judicial exception to a computing environment. Therefore, the additional elements do not integrate the abstract idea into a practical application because they do not impose any meaningful limits on practicing the abstract idea.
Accordingly, even when considered as a whole, the claims do not transform the abstract idea into a patent-eligible invention since the claim limitations do not amount to a practical application or significantly more than an abstract idea for identifying a subset of third-party service providers that are a potential match to provide services to a first client, obtaining a first client cluster that corresponds to the first client, obtaining a first estimate identifying a first weight associated with the first client, obtaining a second estimate, identifying a second weight of the first client, and generating a score for each subset of third-party service providers using the first weight and second weight in a ranking model and providing a notification indicating the scores for the subset of third-party service providers in a computing environment.
Hence, claims 1-4, 6-8, 16-19 and 21-26 are directed to non-statutory subject matter and are rejected as ineligible subject matter under 35 USC 101. See 2019 PEG and MPEP 2106.
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, 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 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.
This application currently names joint inventors. In considering patentability of the claims the examiner presumes that the subject matter of the various claims was commonly owned as of the effective filing date of the claimed invention(s) absent any evidence to the contrary. Applicant is advised of the obligation under 37 CFR 1.56 to point out the inventor and effective filing dates of each claim that was not commonly owned as of the effective filing date of the later invention in order for the examiner to consider the applicability of 35 U.S.C. 102(b)(2)(C) for any potential 35 U.S.C. 102(a)(2) prior art against the later invention.
Claims 1-3, 6-8, 16-18 and 21-26 are rejected under 35 U.S.C. 103 as being unpatentable over Cella et al., US Patent Application Publication No US 2023/0135553 A1, in view of Poms, (WO 2024/006188 A1).
With respect to claims 1, 16 and 24,
Cella discloses,
A non-transitory computer-readable storage medium comprising instructions for a server that, when executed by a processing device, cause the processing device to perform operations comprising: (¶28: “non-transitory computer readable memory that is accessible by at least one processor of the set of processors”; ¶1058: “a computing system (e.g., one or more servers) that may include a processing system 8100 that includes one or more processors, a storage system 8120 that includes one or more computer-readable mediums, and a network interface 8130 that includes one or more communication units”; ¶1593: “one or more general-purpose processing chips that are configured using software instructions or other code, and/or may comprise special-purpose processing chips (e.g., ASICs) customized to perform the functions described herein”)
a memory and one or more processing devices operatively coupled to the memory, the one or more processing devices to perform operations comprising (¶28: “a non-transitory computer readable memory that is accessible by at least one processor of the set of processors”)
identifying, among a plurality of third-party service providers, a subset of third-party service providers that are a potential match to provide services, to a first client, wherein the services provided by the subset of third-party service providers are facilitated via a software-as-a-service (SaaS) management platform (¶5: “as organizations implement CRM systems, ERP systems, operations systems, information technology systems, advanced analytics and other systems that leverage information and information technology, organizations have access to an increasingly wide array of other large data sets, such as marketing data, sales data, operational data, information technology data, performance data, customer data, financial data, market data, pricing data, supply chain data, and the like, including data sets generated by or for the organization and third-party data sets”; ¶67: “a computer-implemented method for facilitating the manufacture and delivery of a 3D printed product to a customer using one or more manufacturing nodes of a distributed manufacturing network”; ¶68: “determining includes matching a customer order with a manufacturing node or a 3D printer based on factors like printer capabilities, locations of the customer and the manufacturing nodes, available capacity at each node, pricing and timelines requirements and the customer satisfaction score”; ¶70: “rating one or more manufacturing nodes based on a customer satisfaction score for meeting customer requirements”; ¶91: “the connectivity facilities include network connections, interfaces, ports, application programming interfaces (APIs), brokers, services, connectors, wired or wireless communication links, human-accessible interfaces, software interfaces, micro-services, SaaS interfaces, PaaS interfaces, IaaS interfaces, cloud capabilities, or the like”; ¶121: “the artificial intelligence system learns on a training set of outcomes, parameters, and data collected from one or more additive manufacturing nodes to provide personalized marketing and customer service with respect to a part or product manufactured and delivered by the one or more manufacturing nodes to a customer”; ¶269: “FIG. 8, the value chain network management platform 604 is illustrated in connection with a set of value chain entities 652 that may be subject to management by the platform 604, may integrate with or into the platform 604, and/or may supply inputs to and/or take outputs from the platform 604, such as ones involved in or for a wide range of value chain activities (such as supply chain activities, logistics activities, demand management and planning activities, delivery activities, shipping activities, warehousing activities, distribution and fulfillment activities, inventory aggregation, storage and management activities, marketing activities, and many others, as involved in various value chain network processes, workflows, activities, events and applications 630 (collectively “applications 630” or simply “activities”)”; ¶479: the machine learning model 3000 may learn by performing cluster analysis, such as by assigning a set of observations into subsets, i.e., clusters, according to one or more predesignated criteria, such as according to a similarity metric of which internal compactness, separation, estimated density, and/or graph connectivity are factors”; ¶1036: “data from many and various third-party data sources 8038 that store third-party data”; ¶1042: “The expert agent system 8008 may receive the interaction data and related features and may generate, train, configure, and/or update an executive agent based thereon… data from various enterprise and/or third-party data sources”; ¶1044: “the types of actions that an expert agent may be trained to perform/recommend include: … an expert agent is configured to determine an action and may output the action to a client application”; ¶1059: “the digital twin configuration system 8102 determines one or more data sources and types of data that feed or otherwise support each object, entity, or state that is depicted in the respective type of digital twin and may determine any internal or external software requests (e.g., API calls) that obtain the identified data types or other suitable data acquisitions mechanisms, such as webhooks, that are configured to automatically receive data from an internal or external data source In some embodiments, the digital twin configuration system 8102 determines internal and/or external software requests that support the identified data types by analyzing the relationships between the different types of data that correspond to a particular state/entity/object and the granularity thereof”; ¶1500: machine learning models may predict, given a sample input, a probability distribution over a set of classes. Thus, rather than outputting only the most likely class to which the sample input should belong, machine learning models can output, for each class, a probability that the sample input belongs to such class “; ¶1155: “Examples of data sources 8020 that may be connected to, associated with, and/or accessed from the CMO digital twin 8308 may include, but are not limited to, a sensor system 8022, a sales database 8024 that is updated with sales figures in real time, a CRM system 8026, a content marketing platform 8028, news websites, a financial database 8030 that tracks costs of the business, surveys 8032 (e.g., customer satisfaction surveys), an org chart 8034, a workflow management system 8036, customer databases 8040 that store customer data, and/or third-party data sources 8038 that store third-party data”; ¶1504: “machine learning models can be included in a recommendation system or engine. As an example, given input data that describes previous outcomes for certain entities (e.g., a score, ranking, or rating indicative of an amount of success or enjoyment), machine learning models can output a suggestion or recommendation of one or more additional entities that, based on the previous outcomes, are expected to have a desired outcome”; ¶1709: “The predictive modeling circuit 9524 may provide the various inputs to the predictive model to generate a prediction, which may comprise one or more discrete and/or continuous values (e.g., predicted scores and/or classifications), one or more confidences, etc”; ¶2766: “the special-purpose system may be partially or fully hosted by a third party offering software as a service (SaaS)”)”
Applicant’s disclosure teaches at ¶56: A “consumer” or a “client organization” (also referred to as ‘client’ herein) can refer to an entity that accesses services from a platform, such as the SaaS management platform provided by a first-party organization (agent) and discusses at ¶157: “The consumer cluster data 226 can include for example one or more of a cluster identifier that identifies a particular consumer cluster; ¶193: “a consumer cluster identifier corresponding to the first consumer. In some embodiments, the consumer cluster identifier identifies, among multiple consumer clusters, a first consumer cluster that corresponds to the first consumer. Giving the broadest reasonable interpretation of applicant’s claim limitation in light of the disclosure, Examiner interprets the customer as taught by Cella as teaching applicant’s consumer and/or client.
obtaining, from a second trained Al model, a second output indicating a first estimate that a response from a first third-party service provider of the subset of third-party service providers to a request for services for the first client cluster will satisfy first client preferences associated with the first client (¶70: “the method further comprises rating one or more manufacturing nodes based on a customer satisfaction score for meeting customer requirements”; Fig 16, ¶317: “The adaptive intelligence systems 808 provide a set of predictions 3070 through the application of artificial intelligence, such as through application of an artificial intelligence system 1160, and optionally through one or more expert systems, machine learning systems, and the like for use with a coordinated set of demand management applications 824 and supply chain applications 812 for a category of goods 3010, which may be produced and sold through the value chain… The adaptive intelligence systems 808 may facilitate applying adapted intelligence capabilities to the coordinated set of demand management applications 824 and supply chain applications 812 such as by producing a set of predictions 3070 that may facilitate coordinating the two sets of value chain applications, or at least facilitate coordinating at least one demand management application and at least one supply chain application from their respective sets”; Fig 17, ¶323: “The adaptive intelligence systems 808 provide a set of classifications 3080 through, for example, the application of artificial intelligence, such as through application of an artificial intelligence system 1160, and optionally through one or more expert systems, machine learning systems, and the like for use with a coordinated set of demand management applications 824 and supply chain applications 812 for a category of goods 3010, which may be produced, marketed, sold, resold, rented, leased, given away, serviced, recycled, renewed, enhanced, and the like through the value chain. The adaptive intelligence systems 808 may deliver the set of classifications 3080 through a set of data processing, artificial intelligence and computational systems 634”; ¶327: “the set of classifications 3080 may be classifications of outcomes for operating a value chain with the coordinated set demand management applications and supply chain applications for the category of goods, so that a user may conduct test cases of coordinated sets of demand management applications and supply chain applications to determine which sets may produce outcomes that are classified as desirable (e.g., viable candidates for a coordinated set of applications) and outcomes that are classified as undesirable”; ¶447: “customer profiling, customer feedback, customer clustering”; ¶460: “ an interface to the application store 3504 may take input from a developer and/or from the platform (such as from an opportunity miner 1460) that indicates one or more attributes of a problem that may be addressed through artificial intelligence and may provide a set of recommendations, such as via an artificial intelligence attribute search engine, for a subset of artificial intelligence solutions that may represent favorable candidates based on the developer’s domain-specific problem. Search results or recommendations may, in embodiments, be based at least in part on collaborative filtering, such as by asking developers to indicate or select elements of favorable models, as well as by clustering, such as by using similarity matrices, k-means clustering, or other clustering techniques that associate similar developers, similar domain-specific problems, and/or similar artificial intelligence solutions”; ¶461: “FIG. 43, the artificial intelligence system 1160 may define a machine learning model 3000 for performing analytics, simulation, decision making, and prediction making related to data processing, data analysis, simulation creation, and simulation analysis of one or more of the value chain entities 652… The machine learning model 3000 may include an artificial neural network, a decision tree, a support vector machine, a Bayesian network, a genetic algorithm, any other suitable form of machine learning model, or a combination thereof”; ¶478: “The machine learning model 3000 may be defined via supervised learning, i.e., one or more algorithms configured to build a mathematical model of a set of training data containing one or more inputs and desired outputs”; Fig 38, ¶554: “artificial intelligence system 2010 leverages one or more machine-learned models 2004 to perform value chain-related tasks on behalf of the value chain system 2030 and/or to make decisions, classifications, and/or predictions on behalf of the value chain system 2030. In some embodiments, a machine learning system 2002 trains the machine learned models 2004 based on training data 2062, outcome data 2060, and/or simulation data 2022. As used herein, the term machine-learned model may refer to any suitable type of model that is learned in a supervised, unsupervised, or hybrid manner. Examples of machine-learned models include neural networks (e.g., deep neural networks, convolution neural networks, and many others), regression-based models, decision trees, hidden forests, Hidden Markov models, Bayesian models, and the like. In embodiments, the artificial intelligence system 2010 and/or the value chain system 2030 may provide outcome data 2060 to the machine-learning system 2002 that relates to a determination (e.g., decision, classification, prediction) made by the artificial intelligence system 2010 based in part on the one or more machine-learned models and the input to those models. The machine learning system may in-turn reinforce/retrain the machine-learned models 2004 based on the feedback. Furthermore, in embodiments, the machine-learning system 2002 may train the machine-learning models based on simulation data 2022 generated by the digital twin simulation system 2020”; ¶711: “artificial intelligence system 1160 may output scores for each possible prediction, where each prediction corresponds to a possible outcome”; ¶1029: “the COO digital twin may be configured to show visual indicators that indicate whether any of the states are at a critical condition, an exceptional condition, or a satisfactory condition. For instance, if employee turnover is very high and employee satisfaction is low, the COO digital twin may depict that the HR-state is at a critical level. In this configuration, the COO may select to drill down into the HR-state, where she may view the employee turnover rate, hiring rate, and employee satisfaction survey results”; ¶1498: “machine learning models are algorithms and/or statistical models that perform specific tasks without using explicit instructions, relying instead on patterns and inference…machine learning models may perform classification, prediction, regression, clustering, anomaly detection, recommendation generation, and/or other tasks”; ¶1499: “machine learning models can perform binary classification, multi-class or multi-label classification. In embodiments, the machine-learning model may output “confidence scores” that are indicative of a respective confidence associated with classification of the input into the respective class. In embodiments, the confidence scores can be compared to one or more thresholds to render a discrete categorical prediction”; ¶2066: “the machine learning models 11520 may provide output data in the form of one or more recommendations. For example, the machine learning models 11520 may be included in a recommendation system or engine. As an example, given input data that describes previous outcomes for certain entities (e.g., a score, ranking, or rating indicative of an amount of success or enjoyment), the machine learning models 11520 may output a suggestion or recommendation of one or more additional entities that, based on the previous outcomes, are expected to have a desired outcome”)
wherein the first client preferences identify a type of services requested, wherein the first estimate identifies a first weight that reflects whether the response from the first third-party service provider will satisfy second client preferences of the first client cluster (¶472: “The machine learning model 3000 may be based on a collection of connected units and/or nodes that may act like artificial neurons that may in some ways emulate neurons in a biological brain. The units and/or nodes may each have one or more connections to other units and/or nodes. The units and/or nodes may be configured to transmit information, e.g., one or more signals, to other units and/or nodes, process signals received from other units and/or nodes, and forward processed signals to other units and/or nodes. One or more of the units and/or nodes and connections therebetween may have one or more numerical “weights” assigned. The assigned weights may be configured to facilitate learning, i.e., training, of the machine learning model 3000. The weights assigned weights may increase and/or decrease one or more signals between one or more units and/or nodes, and in some embodiments may have one or more thresholds associated with one or more of the weights. The one or more thresholds may be configured such that a signal is only sent between one or more units and/or nodes, if a signal and/or aggregate signal crosses the threshold”; ¶1154: ““The CMO digital twin 8308 may utilize the machine learning, A.I. and other analytic capabilities, as described herein, to analyze the content of the four categories of content and classify and score the content characteristics that are probabilistically associated with improved financial or other performance for stated types of marketing campaigns or marketing subject matter. Statistical weights may be applied to such characteristics, where the weight is indicative of a greater degree of financial or some performance metric of interest”; ¶1205: “the types of data that may be depicted in CHRO digital twin 8316 may include, but are not limited to: individual employee data, key performance indicators by business unit, key performance indicators by individual employee, risk management data, regulatory compliance data (e.g., OSHA and EPA compliance data), safety data, diversity data, benefits data (e.g., medical, dental, vision, and health savings accounts (HSA))… decision support data from AI and/or machine learning systems, prediction data from AI and/or machine learning systems, classification data from AI and/or machine learning systems, detection and/or identification data from AI and/or machine learning systems, and the like”; ¶1215: “the AI-reporting tool 8360 may be configured to monitor one or more user-defined key performance indicators (KPIs); ¶1498: “machine learning models are algorithms and/or statistical models that perform specific tasks without using explicit instructions, relying instead on patterns and inference…machine learning models may perform classification, prediction, regression, clustering, anomaly detection, recommendation generation, and/or other tasks”; ¶1499: “machine learning models can perform binary classification, multi-class or multi-label classification. In embodiments, the machine-learning model may output “confidence scores” that are indicative of a respective confidence associated with classification of the input into the respective class. In embodiments, the confidence scores can be compared to one or more thresholds to render a discrete categorical prediction”; ¶2066: “the machine learning models 11520 may provide output data in the form of one or more recommendations. For example, the machine learning models 11520 may be included in a recommendation system or engine. As an example, given input data that describes previous outcomes for certain entities (e.g., a score, ranking, or rating indicative of an amount of success or enjoyment), the machine learning models 11520 may output a suggestion or recommendation of one or more additional entities that, based on the previous outcomes, are expected to have a desired outcome”)
obtaining, from a third trained Al model, a third output indicating a second estimate identifying a second weight that reflects an estimate of occurrences of one or more future life events for a plurality of employees of the first client (¶40: “the platform, such as using an artificial intelligence system, may execute simulations on the digital twin or projected outputs thereof for predicting a possible future state of the distributed manufacturing network entity and/or one or more outputs thereof”; ¶347: “In addition to detecting problem states, the platform 102, such as through the methods of semi-sentient problem recognition, predict a pain point based at least in part on a correlation with a detected problem state… a predicted pain point may be a point of value chain activity further along a supply chain, an activity that occurs in a related activity (e.g., tax planning is related to tax laws), and the like. A predicted pain point may be assigned a risk value based on aspects of the detected problem state and correlations between the predicted pain point activity and the problem state activity”; ¶447: “customer profiling, customer feedback, customer clustering”; ¶472: “the machine learning model 3000 may be and/or include an artificial neural network, e.g., a connectionist system configured to “learn” to perform tasks by considering examples and without being explicitly programmed with task-specific rules. The machine learning model 3000 may be based on a collection of connected units and/or nodes… One or more of the units and/or nodes and connections therebetween may have one or more numerical “weights” assigned. The assigned weights may be configured to facilitate learning, i.e., training, of the machine learning model 3000. The weights assigned weights may increase and/or decrease one or more signals between one or more units and/or nodes, and in some embodiments may have one or more thresholds associated with one or more of the weights”; ¶478: “The machine learning model 3000 may be defined via supervised learning, i.e., one or more algorithms configured to build a mathematical model of a set of training data containing one or more inputs and desired outputs”; Fig 38, ¶554: “a machine learning system 2002 trains the machine learned models 2004 based on training data 2062, outcome data 2060, and/or simulation data 2022”; ¶596: “status information for the value chain network involves a wide range of states, events, workflows, activities, occurrences, or the like”; ¶711: “artificial intelligence system 1160 may output scores for each possible prediction, where each prediction corresponds to a possible outcome”; ¶731: “Customer digital twins 5502 may represent evolving, continuously updated digital representations of value chain network customers 662”; ¶732: “Customer profile digital twins 1730, on the other hand, may represent one or more demographic (age, gender, race, marital status, number of children, occupation, annual income, education level, living status (homeowner, renter, and the like) psychographic, behavioral, economic, geographic, physical (e.g., size, weight, health status, physiological state or condition, or the like) or other attributes of a set of customers”; ¶734: “Customer digital twins 5502 and customer profile digital twins 1730 may allow for modeling, simulation, prediction, decision-making, classification, and the like”; ¶1154: ““The CMO digital twin 8308 may utilize the machine learning, A.I. and other analytic capabilities, as described herein, to analyze the content of the four categories of content and classify and score the content characteristics that are probabilistically associated with improved financial or other performance for stated types of marketing campaigns or marketing subject matter. Statistical weights may be applied to such characteristics, where the weight is indicative of a greater degree of financial or some performance metric of interest”; ¶1205: “the types of data that may be depicted in CHRO digital twin 8316 may include, but are not limited to: individual employee data, key performance indicators by business unit, key performance indicators by individual employee, risk management data, regulatory compliance data (e.g., OSHA and EPA compliance data), safety data, diversity data, benefits data (e.g., medical, dental, vision, and health savings accounts (HSA))… decision support data from AI and/or machine learning systems, prediction data from AI and/or machine learning systems, classification data from AI and/or machine learning systems, detection and/or identification data from AI and/or machine learning systems, and the like”; ¶1215: “the AI-reporting tool 8360 may be configured to monitor one or more user-defined key performance indicators (KPIs)… Additional or alternative KPI metrics may be defined by a user. Examples of these KPI metrics may include… employee turnover percentage, number of reportable health or safety incidents”; ¶1209: “the EMP 8000 may obtain HR-relevant data from the enterprise’s human resources management software (e.g., via an API), human capital software, workforce management software, payroll software, applicant tracking software, accounting software, employee applicant software, publicly disclosed financial statements, third-party reports, tax filings, social media software, job listing websites, recruitment software, and the like”; ¶1210: “, a CHRO digital twin 8316 may provide an interface for an HR executive to perform one or more HR-related workflows. For example, the CHRO digital twin 8316 may provide an interface for an HR-executive to perform, supervise, or monitor workflows, the entities involved in the workflows, and attributes thereof”; ¶1499: “may output “confidence scores” that are indicative of a respective confidence associated with classification of the input into the respective class. In embodiments, the confidence scores can be compared to one or more thresholds to render a discrete categorical prediction”; ¶1747: “the AI system may automatically manage the design, generation and delivery, through use of a set of additive manufacturing units, a highly customized product based on customer specific design requirements, including health requirements, physical configuration requirements, economic factors, and preferences, among many others”; ¶1806: “the artificial intelligence system 10212 may be configured to analyze usage patterns associated with one or more users and learning user preferences with respect to outputs… the system 10212 may develop a profile, such as by the additive manufacturing unit 10102, by location, by user, by organization, by role, or the like… The profile may be used to determine, infer, or suggest preferences of users, organizations, or the like”; ¶1826: “the digital twin may receive queries from a user about the distributed manufacturing network entities, generate responses for the queries and communicate such responses to the user”; ¶2066: “the machine learning models 11520 may provide output data in the form of one or more recommendations. For example, the machine learning models 11520 may be included in a recommendation system or engine. As an example, given input data that describes previous outcomes for certain entities (e.g., a score, ranking, or rating indicative of an amount of success or enjoyment), the machine learning models 11520 may output a suggestion or recommendation of one or more additional entities that, based on the previous outcomes, are expected to have a desired outcome”; ¶2360: “machine learning models 12664 may output a probabilistic classification…machine learning models may predict, given a sample input, a probability distribution over a set of classes. Thus, rather than outputting only the most likely class to which the sample input should belong, machine learning models can output, for each class, a probability that the sample input belongs to such class”)
Examiner interprets at least at least the artificial intelligence system, predicting and providing a score(s) for each possible prediction, and the machine learning model including one or more algorithms configured to statistically analyze data, build a mathematical model of a set of training data (containing one or more inputs and desired outputs), classifying, scoring and applying numerical weights to characteristics (performance metric(s) of interest) as taught by Cella as teaching applicant’s first estimate, second estimate.
generating, by a processing device, a score for each of the subset of third-party service providers using the first weight of the first estimate, and the second weight of the second estimate as parameters in a ranking model,, the score indicating a likelihood that a respective third-party service provider of the subset of third-party service providers is suitable for the first client (¶68: “determining includes matching a customer order with a manufacturing node or a 3D printer based on factors like printer capabilities, locations of the customer and the manufacturing nodes, available capacity at each node, pricing and timelines requirements and the customer satisfaction score”; ¶70: “the method further comprises rating one or more manufacturing nodes based on a customer satisfaction score for meeting customer requirements”; ¶315: “the hybrid adaptive intelligence systems 808 may provide the hybrid artificial intelligence system 3060 that may include a first type of artificial intelligence that is applied to the demand management applications 824 and which is distinct from a second type of artificial intelligence that is applied to the supply chain applications 812. A hybrid artificial intelligence system 3060 may include any combination of types of artificial intelligence systems including a plurality of a first type of artificial intelligence (e.g., neural networks) and at least one second type of artificial intelligence (e.g., an expert system) and the like. In embodiments, a hybrid artificial intelligence system may comprise a hybrid neural network that applies a first type of neural network with respect to the demand management applications 824 and a second type of neural network with respect to the supply chain applications 812”; ¶468: “the machine learning model 3000 may be configured to evaluate a set of hypothetical simulations of one or more of the value chain entities 652. The set of hypothetical simulations may be created by the machine learning model 3000 and the digital twin system 1700 as a result of one or more modeling commands, as a result of one or more modeling goals, one or more modeling commands, by prediction by the machine learning model 3000, or a combination thereof. The machine learning model 3000 may evaluate the set of hypothetical simulations based on one or more metrics defined by the user, one or more metrics defined by the machine learning model 3000, or a combination thereof. In some embodiments, the machine learning model 3000 may evaluate each of the hypothetical simulations of the set of hypothetical simulations independently of one another. In some embodiments, the machine learning model 3000 may evaluate one or more of the hypothetical simulations of the set of hypothetical simulations in relation to one another, for example by ranking the hypothetical simulations or creating tiers of the hypothetical simulations based on one or more metrics”; ¶472: “the machine learning model 3000 may be and/or include an artificial neural network, e.g., a connectionist system configured to “learn” to perform tasks by considering examples and without being explicitly programmed with task-specific rules. The machine learning model 3000 may be based on a collection of connected units and/or nodes… One or more of the units and/or nodes and connections therebetween may have one or more numerical “weights” assigned. The assigned weights may be configured to facilitate learning, i.e., training, of the machine learning model 3000. The weights assigned weights may increase and/or decrease one or more signals between one or more units and/or nodes, and in some embodiments may have one or more thresholds associated with one or more of the weights”; ¶478: “The machine learning model 3000 may be defined via supervised learning, i.e., one or more algorithms configured to build a mathematical model of a set of training data containing one or more inputs and desired outputs”; FIG. 38, ¶554: “artificial intelligence system 2010 leverages one or more machine-learned models 2004 to perform value chain-related tasks on behalf of the value chain system 2030 and/or to make decisions, classifications, and/or predictions on behalf of the value chain system 2030. In some embodiments, a machine learning system 2002 trains the machine learned models 2004 based on training data 2062, outcome data 2060, and/or simulation data 2022. As used herein, the term machine-learned model may refer to any suitable type of model that is learned in a supervised, unsupervised, or hybrid manner. Examples of machine-learned models include neural networks (e.g., deep neural networks, convolution neural networks, and many others), regression-based models, decision trees, hidden forests, Hidden Markov models, Bayesian models, and the like. In embodiments, the artificial intelligence system 2010 and/or the value chain system 2030 may provide outcome data 2060 to the machine-learning system 2002 that relates to a determination (e.g., decision, classification, prediction) made by the artificial intelligence system 2010 based in part on the one or more machine-learned models and the input to those models. The machine learning system may in-turn reinforce/retrain the machine-learned models 2004 based on the feedback. Furthermore, in embodiments, the machine-learning system 2002 may train the machine-learning models based on simulation data 2022 generated by the digital twin simulation system 2020”; ¶711: “artificial intelligence system 1160 may output scores for each possible prediction, where each prediction corresponds to a possible outcome”; ¶1154: “The CMO digital twin 8308 may utilize the machine learning, A.I. and other analytic capabilities, as described herein, to analyze the content of the four categories of content and classify and score the content characteristics that are probabilistically associated with improved financial or other performance for stated types of marketing campaigns or marketing subject matter. Statistical weights may be applied to such characteristics, where the weight is indicative of a greater degree of financial or some performance metric of interest”; ¶1277: “methods and systems described herein that involve an expert system or self-organization capability may use an echo state network (ESN), which may comprise a recurrent neural network with a sparsely connected, random hidden layer. The weights of output neurons may be changed (e.g., the weights may be trained based on feedback)”; ¶1505: “machine learning models can be or include one or more of various different types of machine-learned models. Examples of such different types of machine-learned models are provided below for illustration. One or more of the example models described below can be used (e.g., combined) to provide the output data in response to the input data”; ¶1709: “The predictive modeling circuit 9524 may provide the various inputs to the predictive model to generate a prediction, which may comprise one or more discrete and/or continuous values (e.g., predicted scores and/or classifications), one or more confidences, etc’; ¶1844: “a scoring system 10634 helps with scoring and rating various entities in the distributed manufacturing network 10130, such as based on their performance, quality, timeliness, condition, status, or the like. In embodiments, the scoring system 10634 helps with rating a manufacturing node based on a customer satisfaction score, such as for meeting customer requirements”; ¶1957: “the scoring system 10634 can rate the one or more manufacturing no”; ¶2066: “the machine learning models 11520 may provide output data in the form of one or more recommendations. For example, the machine learning models 11520 may be included in a recommendation system or engine. As an example, given input data that describes previous outcomes for certain entities (e.g., a score, ranking, or rating indicative of an amount of success or enjoyment), the machine learning models 11520 may output a suggestion or recommendation of one or more additional entities that, based on the previous outcomes, are expected to have a desired outcome”)
Applicant’s disclosure only broadly discusses, at ¶48: “The ranking model can rank the subset of producers into a ranked list, with producers that are most likely to match the consumer ranked higher than producers that are less likely to match the consumer. The ranking model can generate this ranked list based on one or more ranking weights (e.g., referred to herein as “weights”). These weights can be determined by one or more AI models that can, for example, infer criteria used by producers to generate responses (e.g., proposals) but are unknown by the agent (and/or consumers”; and ¶84-¶88 generically discusses various AI models ¶84: “an artificial intelligence (AI) model (e.g., also referred to as a machine learning model herein) can include a discriminative AI model (also referred to as discriminative machine learning model herein), a generative AI model (also referred to as generative machine learning model herein) and/or other AI model”; ¶88: “The model 160A and/or the model 160N (also referred to machine learning model or artificial intelligence (AI) model herein) may refer to the model artifact that is created by the training engine 141 using the training data that includes training inputs (e.g., features) and corresponding target outputs (correct answers for respective training inputs) (e.g., labels)”). Examiner interprets at least at least the artificial intelligence system including one or more types of artificial intelligence systems for providing outcome data to a machine learning system related to decision, classification, prediction made by the artificial intelligence system based in part on the one or more (combined) machine learned models and the input(s) to those models, and the scoring system (utilizing performance metrics and numerical weights) for scoring, predicting and rating various entities for meeting customer requirements as taught by Cella as teaching applicant’s score indicating a likelihood that a respective third-party service provider of the subset of third-party service providers is suitable for the first client.
providing a notification indicating the scores for the subset of third-party service providers (¶711: “artificial intelligence system 1160 may output scores for each possible prediction, where each prediction corresponds to a possible outcome… The artificial intelligence system 1160 may then select the outcome with the greater score as the prediction. Alternatively, the system 1160 may output the respective scores to a requesting system”; ¶1158: “Reporting and the content of reporting may be shared by the CMO digital twin 8308 with other executive digital twins, for example, data related to new customers having a particularly high predicted customer lifetime value may be shared with a sales staff for the purpose of exploring cross-selling opportunities”; ¶1168: “comparisons may be used, in part, to produce analytics, scores, reports and the like indicating the relative advantages and/or disadvantages that a company’s product(s) has relative to competitor product(s). In an example, a report may be automatically provided to the marketing department to emphasize the relative advantages that a company product has over a competitor product (e.g., speed of processing) that should be used in a new marketing campaign. Sharing with the marketing department may be accomplished, in part, by the CTO digital twin 8310 communicating with the CMO digital twin 8308 to present reports or other information to the CMO or marketing staff”; ¶2364: “given input data that describes previous outcomes for certain entities (e.g., a score, ranking, or rating indicative of an amount of success or enjoyment), machine learning models can output a suggestion or recommendation of one or more additional entities that, based on the previous outcomes, are expected to have a desired outcome”)
Although Cella does not describe verbatim the wordings of applicant’s claim limitations (first estimate, second estimate, first weight, second weight, ranking model). Cella teaches a management platform of an information technology system, such as a management platform for a value chain of goods and/or services for providing a set of predictions. Cella discloses artificial intelligence systems including one or more types of artificial intelligence systems for providing outcome data to a machine learning system related to decision, classification, and prediction made by the artificial intelligence system based in part on the one or more (combined) machine learned models and the input(s) to those models. Cella further discloses a scoring system (utilizing performance metrics and numerical weights) for scoring, predicting and rating various entities for meeting customer requirements. Cella also teaches that the machine learning models may be included in a recommendation system or engine and that given input data that describes previous outcomes for certain entities (e.g., a score, ranking, or rating indicative of an amount of success or enjoyment), the machine learning models may output a suggestion or recommendation of one or more additional entities that, based on the previous outcomes, are expected to have a desired outcome. Cella discusses hybrid adaptive intelligence systems for providing a plurality of distinct artificial intelligence systems, a hybrid artificial intelligence system, and combinations thereof. Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of applicant’s invention to modify the artificial intelligence system(s), machine learning model(s), and scoring system as taught by Cella for generating a score indicating a likelihood that a respective third-party service provider of the subset of third-party service providers is suitable for the first client. The known artificial intelligence system(s) configured to learn on a training set of outcomes, based on customer requirements, manufacturer ratings, performance characteristics and weights as taught by Cella would have predictably resulted in scoring and rating various entities based on classified characteristics, performance metrics, weights, quality, customer satisfaction and/or requirements via one or more machine learning models, AI models and other analytic capabilities that are probabilistically associated with improved financial or other performance for meeting customer requirements (¶554, ¶711, ¶1505, ¶1844)
Cella teaches clustering algorithms, cluster-based architectures, customer clustering and customer identity management system (such as where an entity is associated with another entity such as an owner, operator, user, or enterprise by an identifier that is assigned by and/or managed by the platform). Cella does not distinctly describe obtaining…a first output indicating a client cluster identifier, but Poms however as shown discloses,
obtaining, from a first trained artificial intelligence (AI) model, a first output indicating a client cluster identifier identifying, among a plurality of client clusters, a first client cluster that corresponds to the first client, wherein clients identified by the first client cluster are grouped based on one or more characteristics shared with the first client (¶9: “Embodiments of the disclosed systems, apparatuses, and methods introduce an approach to semi-automatically generate labels for data based on implementation of a clustering technique and can be used to implement a form of programmatic labeling to accelerate the development of classifiers and other forms of models”; ¶16: “Once the datapoints are initially clustered, the process represents each cluster with a unique aggregate of attributes, typically based on attributes of individual data points in the cluster… For each cluster, the process then trains a classifier to classify datapoints as residing in the cluster or not residing in the cluster… A classifier is developed for each cluster and used to "predict" if a "new" (previously unseen or unclassified) datapoint belongs in that cluster; The process then stores the classifier for each cluster in a database for future reference, and associates a classifier with a cluster using the cluster's unique identifier; For new or previously unclassified datapoints, the process applies the appropriate classifier for each cluster over the datapoints. Each classifier generates a "prediction" or likelihood as to whether the datapoint belongs in the associated cluster. These predictions can be leveraged for use cases including (but not limited to) programmatic labels for training ML models”; ¶104: “Services 512 may include one or more functions or operations for the processing of a set of data, generating representations of the datapoints, forming clusters from the generated representations, and generating labeling functions/labels for data to be used to train a model”; ¶105: “a process or service to authenticate a user wishing to utilize services available through access to the SaaS platform … Based on Similarities Between the Generated Representation for Multiple Datapoints, Form Groups or Clusters of Datapoints; ^ Similarity may be based on a chosen metric and a selected threshold value for inclusion or exclusion from a specific cluster; o Represent Each Formed Group or Cluster by a Unique Identifier; ^ The identifier may be selected by reference to a common attribute of the grouped datapoints, as an example; x In some embodiments, the unique identifier may be generated by a process that determines one or more common features of the grouped datapoints that distinguish them from the members of the other groups or clusters, such as the presence or absence of a characteristic, the presence or absence of a word or phrase, the presence or absence of an object, or a state of a system or process represented by the datapoint; o For Each Group or Cluster, Train a Classifier to Classify a Datapoint as Either Inside or Outside of the Cluster; ^ This will result in a set of classifiers, with one corresponding to each of the groups or clusters; ^ Each such classifier may be evaluated using a set of datapoints to determine the classifier's accuracy and the utility of the assigned identifier (which may later serve as a label for datapoints assigned to the cluster); o Store Each Trained Classifier and Associate the Classifier with the Cluster or Group’s Identifier; o For New Datapoints, Use Each Datapoint as an Input to Each Classifier to Determine the Most Likely Cluster or Group to Which Datapoint Should be Assigned; o Assign a Label to New Datapoint Based on Identifier or Attribute of Cluster or Group to Which it is Assigned; and o Use Plurality of Datapoints and Assigned Labels to Train a Machine Learning or Other Form of Model”)
Applicant’s disclosure teaches at ¶157: “The consumer cluster data 226 can include for example one or more of a cluster identifier that identifies a particular consumer cluster, a cluster membership reflecting the degree to which a consumer belongs to a particular cluster, a cluster centroid that identifies a representative “point” or average dataset for a particular consumer cluster, a value indicating a difference between a dataset of the consumer and the average dataset for a particular cluster, or the like. For example, consumer cluster data 226 can identify a consumer sector, such as, for example, the “tech”, sector or “venture capital (VC)” sector. In some embodiments, the consumer cluster data 226 can identify one or more characteristics that are commonly shared (e.g., similar) across consumers (e.g., consumers 110A-110N) in the same consumer cluster. In some embodiments, consumer cluster data 226 can identify one or more characteristics of specific “adjacent consumers” (e.g., other consumers in the same consumer cluster)”
Cella discloses a distributed manufacturing network information technology system including a cloud-based additive manufacturing management platform with a user interface, connectivity facilities, data storage facilities, and monitoring facilities. Cella further discloses that distributed manufacturing network information technology system includes an artificial intelligence system configured to learn on a training set of outcomes, parameters, and data collected via various applications, and connectivity facilities/ network connections/interfaces and the like. Cella further teaches clustering algorithms, cluster-based architectures, customer clustering and customer identity management system (such as where an entity 652 is associated with another entity 652, such as an owner, operator, user, or enterprise by an identifier that is assigned by and/or managed by the platform 604). Poms teaches clustering techniques which can be used to implement a form of programmatic labeling to accelerate the development of classifiers and other forms of models. Poms further teaches utilizing/training via machine learning or any other model to Use Each Datapoint as an Input to Each Classifier to Determine the Most Likely Cluster or Group to Which Datapoint Should be Assigned; or Assign a Label to New Datapoint Based on Identifier or Attribute of Cluster or Group to Which it is Assigned”
A person of ordinary skill in the art before the expected filing date of applicant’s invention would have been motivated to modify the distributed manufacturing network information technology system of Cella with the known clustering techniques as taught by Poms to achieve the claimed invention and there would have been a reasonable expectation of success in doing so. DyStar Textilfarben GmbH & Co. Deutschland KG v. C.H. Patrick Co., 464 F.3d 1356, 1360, 80 USPQ2d 1641, 1645 (Fed. Cir. 2006)
Cella and Poms are directed to the same field of endeavor since they are related to providing/delivering business-related or other applications, functions, workflows and/or services to users via a software-as-a-service platform in a computing environment. Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to combine the distributed manufacturing network information technology system of Cella with the method/system for labeling of training data for machine learning models via clustering techniques as taught by Poms since it allows for storing, training classifier(s) and associating each trained classifier with a unique cluster or group identifier (¶9, ¶16, ¶104, ¶105)
Independent claims 16 and 24 recite substantially similar limitations as independent claim 1 and are rejected based on the same rationale noted above.
With respect to claims 2, 17 and 25,
Cella and Poms disclose all of the above limitations, Cella further discloses,
wherein identifying, among the plurality of third-party service providers, the subset of third-party service providers is based on one or more criteria related to characteristics of the first client and characteristics of the plurality of third-party service providers that provide, via the SaaS management platform, one or more services (¶61: “the artificial intelligence system executes simulations on one or more of the part twins, the product twins, the printer twins and the manufacturing node twins for optimizing the production sequencing of parts and products based on quoted price, delivery, sale margin, order size, or similar characteristics”; ¶67: “Aspects provided herein include a computer-implemented method for facilitating the manufacture and delivery of a 3D printed product to a customer using one or more manufacturing nodes of a distributed manufacturing network, comprising receiving one or more product requirements from the customer; tokenizing and storing the product requirements in a distributed ledger system; determining one or more manufacturing nodes, printers, processes and materials based on the product requirements”; ¶68: “determining includes matching a customer order with a manufacturing node or a 3D printer based on factors like printer capabilities, locations of the customer and the manufacturing nodes, available capacity at each node, pricing and timelines requirements and the customer satisfaction score”; ¶362:“the platform 604 may include, integrate, integrate with, manage, control, coordinate with, or otherwise handle any of a wide variety of digital twins 1700, such as… customer twins 1730 (such as representing physical, behavioral, demographic, psychographic, financial, historical, affinity, interest, and other characteristics of groups of customers or individual customers”; ¶1838: The manufacturing workflow management applications 10208 may manage the various workflows, events and applications related to production or printing and value chain management. In embodiments, a matching system 10632 may help with matching a set of customer orders with a set of additive manufacturing units 10102 or manufacturing nodes. Orders may include firm orders, contingent orders (e.g., based on price contingency, timing contingency or other factors), aggregated orders, custom orders, volume orders, time-based orders, and others. In embodiments, orders may be expressed in smart contracts, such as operating on a set of blockchains. The matching may be based on factors like additive manufacturing capabilities, locations of the customer and the manufacturing nodes, “)
With respect to claims 3, 18 and 26,
Cella and Poms disclose all of the above limitations, Cella further discloses,
providing a first input to the first trained AI model, the first input comprising: first client data pertaining to the first client the first client data identifying one or more characteristics of the first client (¶460: “an interface to the application store 3504 may take input from a developer and/or from the platform (such as from an opportunity miner 1460) that indicates one or more attributes of a problem that may be addressed through artificial intelligence and may provide a set of recommendations, such as via an artificial intelligence attribute search engine, for a subset of artificial intelligence solutions that may represent favorable candidates based on the developer’s domain-specific problem. Search results or recommendations may, in embodiments, be based at least in part on collaborative filtering, such as by asking developers to indicate or select elements of favorable models, as well as by clustering, such as by using similarity matrices, k-means clustering, or other clustering techniques that associate similar developers, similar domain-specific problems, and/or similar artificial intelligence solutions”; ¶461: “The machine learning model 3000 builds one or more mathematical models based on training data to make predictions and/or decisions without being explicitly programmed to perform the specific tasks. The machine learning model 3000 may receive inputs of sensor data as training data, including event data 1034 and state data 1140 related to one or more of the value chain entities 652. The sensor data input to the machine learning model 3000 may be used to train the machine learning model 3000 to perform the analytics, simulation, decision making, and prediction making relating to the data processing, data analysis, simulation creation, and simulation analysis of the one or more of the value chain entities”;¶478: “The machine learning model 3000 may be defined via supervised learning, i.e., one or more algorithms configured to build a mathematical model of a set of training data containing one or more inputs and desired outputs”)
With respect to claims 6 and 21,
Cella and Poms disclose all of the above limitations, Cella further discloses,
providing a second input to the second trained AI model, the second input comprising: the client cluster identifier corresponding to the first client (¶107: “the artificial intelligence system is configured to automatically classify and cluster parts, such as ones that may be additively manufactured, such as based on similarity of attributes, including physical attributes, shapes, functional attributes, material attributes, performance attributes, economic attributes, and others”; ¶297: “the value chain monitoring systems layer 614 and its data collection systems 640 may include an entity discovery system 1900 for discovering one or more value chain network entities 652… by identity management systems (such as where an entity 652 is associated with another entity 652, such as an owner, operator, user, or enterprise by an identifier that is assigned by and/or managed by the platform 604), and the like”; ¶447: “ the set of supply chain applications and demand management applications includes, for example and without limitation one or more involving…customer identity management…customer clustering”;¶460: “an artificial intelligence store 3504 may include a set of interfaces to artificial intelligence systems, such as enabling the download of relevant artificial intelligence applications, establishment of links or other connections to artificial intelligence systems (such as links to cloud-deployed artificial intelligence systems via APIs, ports, connectors, or other interfaces) and the like. The artificial intelligence store 3504 may include descriptive content with respect to each of a variety of artificial intelligence systems, such as metadata or other descriptive material indicating suitability of a system for solving particular types of problems (e.g., forecasting, NLP, image recognition, pattern recognition, motion detection, route optimization, or many others) and/or for operating on domain-specific inputs, data or other entities. In embodiments, the artificial intelligence store 3504 may be organized by category, such as domain, input types, processing types, output types, computational requirements and capabilities, cost, energy usage, and other factors… an interface to the application store 3504 may take input from a developer and/or from the platform (such as from an opportunity miner 1460) that indicates one or more attributes of a problem that may be addressed through artificial intelligence and may provide a set of recommendations, such as via an artificial intelligence attribute search engine, for a subset of artificial intelligence solutions that may represent favorable candidates based on the developer’s domain-specific problem”; ¶461: “FIG. 43, the artificial intelligence system 1160 may define a machine learning model 3000 for performing analytics, simulation, decision making, and prediction making related to data processing, data analysis, simulation creation, and simulation analysis of one or more of the value chain entities 652… The machine learning model 3000 may include an artificial neural network, a decision tree, a support vector machine, a Bayesian network, a genetic algorithm, any other suitable form of machine learning model, or a combination thereof”; ¶478: “The machine learning model 3000 may be defined via supervised learning, i.e., one or more algorithms configured to build a mathematical model of a set of training data containing one or more inputs and desired outputs”; ¶1715: “a wide variety of inputs 9592 may be used, including enterprise resource planning system inputs (e.g., inventory, pricing, accounting, sales, employee information), customer relationship management system inputs (e.g., customer data, payment methods, etc.), security system inputs (e.g., data access and management, surveillance video, authentication data), inputs comprising crime statistics, police reports, cost of living reports, and the like …provide recommendations based on the classifications and predictions (e.g., health or psychological screenings, security authentications/evaluations, etc …automatically analyze and classify pathology data (e.g., to detect diseases, population health, disease prevalence and spread, etc.), to provide predictions based on the pathology data and classifications (e.g., disease spread, population changes, health care costs, etc.), and to provide recommendations based on the classifications and predictions (e.g., quarantines, allocation of medical resources, adjustment of insurance premiums, etc.”)
Cella discloses all of the above limitations, Cella does not distinctly describe a client cluster identifier, but Poms however as shown discloses,
the client cluster identifier corresponding to the first client (¶9: “Embodiments of the disclosed systems, apparatuses, and methods introduce an approach to semi-automatically generate labels for data based on implementation of a clustering technique and can be used to implement a form of programmatic labeling to accelerate the development of classifiers and other forms of models”; ¶16: “Once the datapoints are initially clustered, the process represents each cluster with a unique aggregate of attributes, typically based on attributes of individual data points in the cluster… For each cluster, the process then trains a classifier to classify datapoints as residing in the cluster or not residing in the cluster… A classifier is developed for each cluster and used to "predict" if a "new" (previously unseen or unclassified) datapoint belongs in that cluster; The process then stores the classifier for each cluster in a database for future reference, and associates a classifier with a cluster using the cluster's unique identifier; For new or previously unclassified datapoints, the process applies the appropriate classifier for each cluster over the datapoints. Each classifier generates a "prediction" or likelihood as to whether the datapoint belongs in the associated cluster. These predictions can be leveraged for use cases including (but not limited to) programmatic labels for training ML models”; ¶104: “Services 512 may include one or more functions or operations for the processing of a set of data, generating representations of the datapoints, forming clusters from the generated representations, and generating labeling functions/labels for data to be used to train a model”; ¶105: “…a process or service to authenticate a user wishing to utilize services available through access to the SaaS platform … Based on Similarities Between the Generated Representation for Multiple Datapoints, Form Groups or Clusters of Datapoints; ^ Similarity may be based on a chosen metric and a selected threshold value for inclusion or exclusion from a specific cluster; o Represent Each Formed Group or Cluster by a Unique Identifier; ^ The identifier may be selected by reference to a common attribute of the grouped datapoints, as an example; x In some embodiments, the unique identifier may be generated by a process that determines one or more common features of the grouped datapoints that distinguish them from the members of the other groups or clusters, such as the presence or absence of a characteristic, the presence or absence of a word or phrase, the presence or absence of an object, or a state of a system or process represented by the datapoint; o For Each Group or Cluster, Train a Classifier to Classify a Datapoint as Either Inside or Outside of the Cluster; ^ This will result in a set of classifiers, with one corresponding to each of the groups or clusters; ^ Each such classifier may be evaluated using a set of datapoints to determine the classifier's accuracy and the utility of the assigned identifier (which may later serve as a label for datapoints assigned to the cluster); o Store Each Trained Classifier and Associate the Classifier with the Cluster or Group’s Identifier; o For New Datapoints, Use Each Datapoint as an Input to Each Classifier to Determine the Most Likely Cluster or Group to Which Datapoint Should be Assigned; o Assign a Label to New Datapoint Based on Identifier or Attribute of Cluster or Group to Which it is Assigned; and o Use Plurality of Datapoints and Assigned Labels to Train a Machine Learning or Other Form of Model”)
Cella discloses a distributed manufacturing network information technology system including a cloud-based additive manufacturing management platform with a user interface, connectivity facilities, data storage facilities, and monitoring facilities. Cella further discloses that distributed manufacturing network information technology system includes an artificial intelligence system configured to learn on a training set of outcomes, parameters, and data collected via various applications, and connectivity facilities/ network connections/interfaces and the like. Cella teaches clustering algorithms, cluster-based architectures, customer clustering and customer identity management system (such as where an entity 652 is associated with another entity 652, such as an owner, operator, user, or enterprise by an identifier that is assigned by and/or managed by the platform 604).
Poms teaches clustering techniques which can be used to implement a form of programmatic labeling to accelerate the development of classifiers and other forms of models. Poms further teaches utilizing/training via machine learning or any other model to Use Each Datapoint as an Input to Each Classifier to Determine the Most Likely Cluster or Group to Which Datapoint Should be Assigned; o Assign a Label to New Datapoint Based on Identifier or Attribute of Cluster or Group to Which it is Assigned”
Cella and Poms are directed to the same field of endeavor since they are related to an architecture for providing/delivering business-related or other applications, functions, workflows and/or services to users in a computing environment. Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to combine the distributed manufacturing network information technology system of Cella with the method/system for labeling of training data for machine learning models via clustering techniques as taught by Poms since it allows for storing, training a classifier(s) and associating each trained classifier with a unique cluster or group identifier (¶9, ¶16, ¶104, ¶105).
Cella further discloses,
third-party service provider data pertaining to the first third-party service provider and external factor data identifying one or more factors external to and that affect at least one of the first third-party service provider or the first client (¶460: “an interface to the application store 3504 may take input from a developer and/or from the platform (such as from an opportunity miner 1460) that indicates one or more attributes of a problem that may be addressed through artificial intelligence and may provide a set of recommendations, such as via an artificial intelligence attribute search engine, for a subset of artificial intelligence solutions that may represent favorable candidates based on the developer’s domain-specific problem. Search results or recommendations may, in embodiments, be based at least in part on collaborative filtering… The artificial intelligence store 3504 may include e-commerce features, such as ratings, reviews, links to relevant content, and mechanisms for provisioning, licensing, delivery and payment (including allocation of payments to affiliates and or contributors), including ones that operate using smart contract and/or blockchain features to automate purchasing, licensing, payment tracking, settlement of transactions, or other features”; ¶478: “The machine learning model 3000 may be defined via supervised learning, i.e., one or more algorithms configured to build a mathematical model of a set of training data containing one or more inputs and desired outputs”; ¶554: “FIG. 38 illustrates example embodiments of a system for controlling and/or making decisions, predictions, and/or classification on behalf of a value chain system 2030. In embodiments, an artificial intelligence system 2010 leverages one or more machine-learned models 2004 to perform value chain-related tasks on behalf of the value chain system 2030 and/or to make decisions, classifications, and/or predictions on behalf of the value chain system 2030. In some embodiments, a machine learning system 2002 trains the machine learned models 2004 based on training data 2062, outcome data 2060, and/or simulation data 2022… In embodiments, the artificial intelligence system 2010 and/or the value chain system 2030 may provide outcome data 2060 to the machine-learning system 2002 that relates to a determination (e.g., decision, classification, prediction) made by the artificial intelligence system 2010 based in part on the one or more machine-learned models and the input to those models. The machine learning system may in-turn reinforce/retrain the machine-learned models 2004 based on the feedback. Furthermore, in embodiments, the machine-learning system 2002 may train the machine-learning models based on simulation data 2022 generated by the digital twin simulation system 2020”)
With respect to claims 7 and 22,
Cella and Poms disclose all of the above limitations, Cella further discloses,
further comprising: obtaining, from a third trained Al model, a fourth output indicating a likelihood that the first client will consume the services provided by the subset of third-party service providers, wherein generating the score for each of the subset of third-party service providers is based on one or more outputs comprising the fourth output. (¶277: “Referring still to FIG. 10, the set of applications 614 provided on the VCNP 604, integrated with the VCNP 604 and/or managed by or for the VCNP 604 and/or involving a set of value chain network entities 652 may further include, without limitation: a predictive maintenance application 910…demand prediction application…customer profiling application”; ¶286: “A wide range of data types may be stored in the storage layer 624 using various storage media and data storage types, data architectures 1002, and formats, including, without limitation:…event data …claims data 664 (such as relating to insurance claims, such as for business interruption insurance, product liability insurance, insurance on goods, facilities, or equipment, flood insurance, insurance for contract-related risks, and many others, as well as claims data relating to product liability, general liability, workers compensation, injury and other liability claims and claims data relating to contracts, such as supply contract performance claims, product delivery requirements, warranty claims, indemnification claims, delivery requirements, timing requirements, milestones, key performance indicators and others”;¶460, ¶461: “The machine learning model 3000 may include an artificial neural network, a decision tree, a support vector machine, a Bayesian network, a genetic algorithm, any other suitable form of machine learning model, or a combination thereof”; ¶478: “The machine learning model 3000 may be defined via supervised learning, i.e., one or more algorithms configured to build a mathematical model of a set of training data containing one or more inputs and desired outputs”; ¶554: “FIG. 38 … an artificial intelligence system 2010 leverages one or more machine-learned models 2004 to perform value chain-related tasks on behalf of the value chain system 2030 and/or to make decisions, classifications, and/or predictions on behalf of the value chain system 2030… a machine learning system 2002 trains the machine learned models 2004 based on training data 2062, outcome data 2060, and/or simulation data 2022… the artificial intelligence system 2010 and/or the value chain system 2030 may provide outcome data 2060 to the machine-learning system 2002 that relates to a determination (e.g., decision, classification, prediction) made by the artificial intelligence system 2010 based in part on the one or more machine-learned models and the input to those models”; ¶711: “artificial intelligence system 1160 may output scores for each possible prediction, where each prediction corresponds to a possible outcome”; ¶1504: “machine learning models can provide output data in the form of one or more recommendations. For example, machine learning models can be included in a recommendation system or engine. As an example, given input data that describes previous outcomes for certain entities (e.g., a score, ranking, or rating indicative of an amount of success or enjoyment), machine learning models can output a suggestion or recommendation of one or more additional entities that, based on the previous outcomes, are expected to have a desired outcome”; ¶2066: “given input data that describes previous outcomes for certain entities (e.g., a score, ranking, or rating indicative of an amount of success or enjoyment), the machine learning models 11520 may output a suggestion or recommendation of one or more additional entities that, based on the previous outcomes, are expected to have a desired outcome”)
With respect to claims 8 and 23,
Cella and Poms disclose all of the above limitations, Cella further discloses,
wherein the one or more future life events comprises at least one of a change in marital status or a change in a number of dependents (represent one or more demographic (age, gender, race, marital status, number of children, occupation, annual income, education level, living status (homeowner, renter, and the like) psychographic, behavioral, economic, geographic, physical (e.g., size, weight, health status, physiological state or condition, or the like) or other attributes of a set of customers”; ¶733: “ Customers 662 can be depicted in a set of one or more customer digital twins 5502, such as by populating the customer digital twin 1730 with value chain network data objects 1004, such as event data 1034, state data 1140, or other data with respect to value chain network customers 662. Likewise, customer profiles 5504 can be depicted in a set of one or more customer profile digital twins 1730, such as by populating the customer profile digital twins 1730 with value chain network data objects 1004;” ¶735: “Customer digital twins 5502 and customer profile digital twins 1730 may allow for modeling, simulation, prediction, decision-making, classification, and the like”;he EMP may include artificial intelligence systems that have been trained and/or configured to provide automated understanding of organizational structures and relationships, automated configuration of digital twins for roles within an organization based on the understanding of structures and relationships, and automated configuration of digital twin parameters, settings, and features based on the role and/or the identity of the user filling the role (including the competencies, education, experience, training, or the like of the user)”; ¶1018: “executive digital twins may allow a user (e.g., a CEO, CFO, COO, VP, Board member, GC, or the like) to increase the granularity of a particular state depicted in the digital twin (also referred to “drilling down into” a state of the digital twin)… the CEO digital twin may depict different colored icons to differentiate a condition (e.g., current and/or forecasted condition) of a respective data item. For example, a red icon may indicate a warning state, a yellow icon may indicate a neutral state, and a green icon may indicate a satisfactory state. In this example, the user (e.g., a CEO) may drill down into a particular data item (e.g., may select a red sales icon to drill down into the sales data, to see more specific and/or additional data, in order to determine why there is the warning state). In response, the CEO digital twin may depict one or more different data streams relating to the selected data item”; ¶1205: “the types of data that may be depicted in CHRO digital twin 8316 may include, but are not limited to: individual employee data, key performance indicators by business unit, key performance indicators by individual employee, risk management data, regulatory compliance data (e.g., OSHA and EPA compliance data), safety data, diversity data, benefits data (e.g., medical, dental, vision, and health savings accounts (HSA)) compensation data, compensation comparison data, compensation trend data”; ¶1215: “the AI-reporting tool 8360 may be configured to monitor one or more user-defined key performance indicators (KPIs). Examples of KPIs of an enterprise may include, but are not limited to, with respect to systems, facilities, processes, functions, or workforce units... safety metrics, health metrics…KPI metrics may include…employee turnover percentage, number of reportable health or safety incidents”; ¶1714: “Based on the monitored outcomes, the feedback circuit 9538 may adjust (e.g., retrain) any models used by the classification module 9510, prediction module 9520, and/or recommendation module 9530” ¶1715: “a wide variety of inputs 9592 may be used, including enterprise resource planning system inputs (e.g., inventory, pricing, accounting, sales, employee information), customer relationship management system inputs (e.g., customer data, payment methods, etc.), security system inputs (e.g., data access and management, surveillance video, authentication data), inputs comprising crime statistics, police reports, cost of living reports, and the like …provide recommendations based on the classifications and predictions (e.g., health or psychological screenings, security authentications/evaluations, etc …automatically analyze and classify pathology data (e.g., to detect diseases, population health, disease prevalence and spread, etc.), to provide predictions based on the pathology data and classifications (e.g., disease spread, population changes, health care costs, etc.), and to provide recommendations based on the classifications and predictions (e.g., quarantines, allocation of medical resources, adjustment of insurance premiums, etc.”)
Claims 4 and 19 are rejected under 35 U.S.C. 103 as being unpatentable over Cella, Poms in further view of Jalal et al., US Patent Application Publication No US 2023/0259654 A1.
With respect to claims 4 and 19,
Cella and Poms disclose all of the above limitations, the combination of Cella and Poms does not distinctly describe the following limitations but Jalal however as shown discloses,
the operations further comprising: performing a pre-processing operation on the first client data to generate first anonymized client data, (Abstract: “techniques for privacy-preserving computing to protect a subject's privacy while using the subject's data for secondary purposes such as training and deploying artificial intelligence tools… a de-identifying operation, an anonymizing operation, or both on the subject data … receiving a production model from the remote server, the production model including parameters derived in part from the processed subject data”; ¶10: “the de-identifying operation, the anonymizing operation, or both are performed on the individually identifiable health information of the subject data based on the set of data regulations”; ¶27: “the processed subject data having been de-identified, anonymized, or both”)
wherein the pre-processing operation transforms the first client data to prevent identification of individuals using the first anonymized client data, and wherein the first input comprises the first anonymized client data (¶5: “storing the processed subject data in a processed data store accessible to the local cloud server; sending a batch of data to a remote cloud server, the batch of data comprising the processed subject data; receiving a production model from the remote cloud server, the production model including parameters derived in part from the processed subject data; receiving subsequent data regarding a second subject from a second computing device associated with the second subject; inputting the subsequent data into the production model to analyze the subsequent data and generate an inference or prediction from the analysis of the subsequent data”;¶25: “performing the de-identifying operation, the anonymizing operation, or both on the response data to generate processed response data; storing the processed response data in the processed data store; and sending a batch of data to the remote cloud server, the batch of data comprising the processed response data.¶27: “ the processed subject data having been de-identified, anonymized, or both; ¶53: “The local server is adapted to receive data and perform a de-identifying and/or anonymizing operation on the data to generate privacy-protected data”; ¶54: ““anonymizing” is the act of permanently and completely removing personal identifiers from data, such as converting personally identifiable information into aggregated data. Anonymized data is data that can no longer be associated with an individual in any manner”)
Cella teaches a customer digital twin which may be used to generate an anonymized customer digital twin 5902. The platform may include, integrate, integrate with, manage, control, coordinate with, or otherwise handle anonymized customer digital twins 5902. Anonymized customer digital twins 5902 may be an anonymized digital representation of a customer 714. In embodiments, anonymized customer digital twins 5902 are not populated with personally identifiable information but may otherwise be populated using the same data sources as its corresponding customer digital twin 5502. Poms teaches clustering techniques which can be used to implement a form of programmatic labeling to accelerate the development of classifiers and other forms of models. Poms further teaches utilizing/training via machine learning or any other model to Use Each Datapoint as an Input to Each Classifier to Determine the Most Likely Cluster or Group to Which Datapoint Should be Assigned; or Assign a Label to New Datapoint Based on Identifier or Attribute of Cluster or Group to Which it is Assigned”
Jalal distinctly discloses techniques for privacy-preserving computing to protect a subject’s privacy while using the subject’s data for secondary purposes such as training and deploying artificial intelligence tools.
Cella, Poms and Jalal are directed to the same field of endeavor since they are related to providing, training and deploying artificial intelligence tools/solutions based on user data in a computing environment. Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to combine the distributed manufacturing network information technology system of Cella, and the method/system for labeling of training data for machine learning models via clustering techniques of Poms with the privacy-preserving computing techniques as taught by Jalal since it allows for the collection, de-identification, anonymization, storing and processing of user data to remove and/or manipulate personally identifiable information (Abstract, ¶10, ¶25, ¶27, ¶53, ¶54, ¶76).
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure.
Choudhary et al., (WO2025/174737 A1) “Methods and Systems for Determining Optimal Store Locations for Aggregate Merchants”, relating to accessing a transaction feature set, and a social media feature set for each merchant location from a database. Method includes labeling each merchant location with an active or inactive merchant flag and generating a geospatial merchant graph based on the corresponding transaction feature set, the corresponding social media feature set, and the corresponding label of each merchant location.
Khaligh et al., US Patent Application Publication No US 2022/0261661 A1,” Methods, Systems, Articles of Manufacture and Apparatus to Improve Job Scheduling Efficiency” relating to resource consumption management, and, methods, systems, articles of manufacture and apparatus to improve job scheduling efficiency.
Amjadi, US Patent Application Publication No US 2019/0172116 A1, “Computer Program, Method, and System for Matching Consumers with Service Providers”, relating to matching consumers in need of services with service providers capable of rendering such services.
Schoenberg, US Patent Application Publication No US 2014/0006058 A1, “Connecting Service Providers and Consumers of Services”, teaches a request is received from a consumer of services to consult with a service provider having a service provider profile that satisfies at least some attributes in a set of attributes that define a suitable service provider; an available service provider satisfying at least some of the attributes in the set of attributes is identified; and a communication channel is provided to establish a communication between the consumer of services and the identified service provider.
Goad et al., US Patent Application Publication No 2014/0149249 A1, “Systems and Methods for Managing and/or Recommending Third Party Products and Services Provided to a User”, relating to electronic management and recommendation of products and services provided by third parties to consumers via a product and service management portal.
Conclusion
Applicant's amendment necessitated the new ground(s) of rejection presented in this Office action. Accordingly, THIS ACTION IS MADE FINAL. See MPEP § 706.07(a). Applicant is reminded of the extension of time policy as set forth in 37 CFR 1.136(a).
A shortened statutory period for reply to this final action is set to expire THREE MONTHS from the mailing date of this action. In the event a first reply is filed within TWO MONTHS of the mailing date of this final action and the advisory action is not mailed until after the end of the THREE-MONTH shortened statutory period, then the shortened statutory period will expire on the date the advisory action is mailed, and any nonprovisional extension fee (37 CFR 1.17(a)) pursuant to 37 CFR 1.136(a) will be calculated from the mailing date of the advisory action. In no event, however, will the statutory period for reply expire later than SIX MONTHS from the mailing date of this final action.
Any inquiry of a general nature or relating to the status of this application or concerning this communication or earlier communications from the Examiner should be directed to Kimberly L. Evans whose telephone number is 571.270.3929. The Examiner can normally be reached on Monday-Friday, 9:30am-5:00pm. If attempts to reach the examiner by telephone are unsuccessful, the Examiner’s supervisor, Lynda Jasmin can be reached at 571.272.6782.
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/KIMBERLY L EVANS/Examiner, Art Unit 3629
/NATHAN C UBER/Supervisory Patent Examiner, Art Unit 3626