DETAILED ACTION
This rejection is in response to application filed 05/03/2024.
Claims 1-20 are currently pending and have been examined.
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 .
Claim Rejections - 35 USC § 112(b)
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 1-20 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, 11, and 16 recite: determine an optimal action or path for the customer; matching, via a match engine, the customer with one or more of the partners based on the software usage data of the customer, the optimal action or path, rendering said claims indefinite because it is unclear whether the first recitation of path is the same or different than the subsequent recitation of path. Appropriate correction or clarification is required.
Claims 5, 15, and 20 recite: path, rendering said claims indefinite because it is unclear whether the first recitation of path in independent claims 1, 11, and 16 are the same or different than the subsequent recitation of path. Appropriate correction or clarification is required.
There is insufficient antecedent basis for this limitation in:
Claim 1, 11, and 16 recite: the documentation;
Claims 2-3, 12-13, and 17-18 recite: the one or more contextual features;
Claim 10 recites: the AI model.
Appropriate correction or 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 1-20 are rejected under 35 U.S.C. 101 because the claimed invention is directed to a judicial exception (an abstract idea) without significantly more.
Under Step 1 of the Subject Matter Eligibility Test, it must be considered whether the claims are directed to one of the four statutory classes of invention. See MPEP § 2106. In the instant case, claims 1-10 are directed to a system, claims 11-15 are directed to a method, and claims 16-20 are directed to a non-transitory computer readable medium (which falls within one of the four statutory categories of invention (process/apparatus). Accordingly, the claims will be further analyzed under revised step 2:
Under step 2A (prong 1) of the Subject Matter Eligibility Test, it must be considered whether the claims recite a judicial exception if so, then determine in Prong Two if the recited judicial exception is integrated into a practical application of that exception. If the claim recites a judicial exception (i.e., an abstract idea), the claim requires further analysis in Prong Two. One of the enumerated groupings of abstract ideas is defined as certain methods of organizing human activity that includes fundamental economic principles or practices (including hedging, insurance, mitigating risk); commercial or legal interactions (including agreements in the form of contracts; legal obligations; advertising, marketing or sales activities or behaviors; business relations); managing personal behavior or relationships or interactions between people (including social activities, teaching, and following rules or instructions). See MPEP § 2106.04(a)(2).
Regarding representative independent claim 1, recites the abstract idea of:
receiving a call requesting a first generative model to generate a partner recommendation for a customer of an entity;
constructing, via a prompt construction unit, a first prompt by appending to a first instruction string documentation submitted by partners of the entity and historical execution metrics associated with the partners, the first instruction string including instructions to the first generative model to determine capability data associated with the partners based on the documentation and the historical execution metrics;
providing as an input the documentation and the historical execution metrics to the first generative model and receiving as an output the capability data associated with the partners from the first generative model;
determining software usage data of the customer …based on telemetry data and cloud data associated with the customer;
processing the software usage data of the customer and contextual data associated with the customer using a usage progression model to determine an optimal action or path for the customer;
matching, via a match engine, the customer with one or more of the partners based on the software usage data of the customer, the optimal action or path, and the capability data associated with the partners; and
providing for display the matched one or more of the partners…associated with the entity, the customer, or the matched one or more of the partners.
The above-recited limitations amounts to certain methods of organizing human activity as it relates to sales activities and commercial interactions because the claim recites recommending partners for a customer in response to a request based on matching data determined for the customer with the data determined for the partners as well as determining for the data for the partners and customer. Accordingly, the claim recites an abstract idea. See MPEP § 2106.
The Step 2A (prong 2) of the Subject Matter Eligibility Test, is the next step in the eligibility analyses and looks at whether the abstract idea is integrated into a practical application. This requires an additional element or combination of additional elements in the claims to apply, rely on, or use the judicial exception in a manner that imposes a meaningful limit on the judicial exception, such that the claim is more than a drafting effort designed to monopolize the exception. See MPEP § 2106.
In this instance, the claims recite the additional elements such as:
A data processing system comprising: a processor, and a machine-readable storage medium storing executable instructions which, when executed by the processor, cause the processor alone or in combination with other processors to perform the following operations: … using a first artificial intelligence (AI) model … to a client device ...(Claim 1, 11, and 16);
…the first AI model is a customer usage machine learning model, and the machine-readable storage medium further includes instructions configured to cause the processor alone or in combination with other processors to perform operations of: training the customer usage machine learning model by labelling contextual features of the telemetry data and the cloud data associated with the customer, and inputting the labelled contextual features and training data to the customer usage machine learning model, (Claims 2, 12, and 17);
wherein the machine-readable storage medium further includes instructions configured to cause the processor alone or in combination with other processors to perform operations of:..(Claim 3, 5, 6, 8);
wherein the machine-readable storage medium further includes instructions configured to cause the processor alone or in combination with other processors to perform operations of: … using a second AI model… (Claim 4, 14, and 19);
wherein the machine-readable storage medium further includes instructions configured to cause the processor alone or in combination with other processors to perform operations of:… using the second AI model ...(Claim 7);
…using the AI model (Claim 10);
A non-transitory computer readable medium on which are stored instructions that, when executed, cause a programmable device to perform functions of:..(Claim 16).
However, these elements do not amount to an improvement in the functioning of a computer or any other technology or technical field, apply the judicial exception with, or by use of, a particular machine, or apply or use the judicial exception in some other meaningful way beyond generally linking the use of the judicial exception to a particular technological environment, such that the claim as a whole is more than a drafting effort designed to monopolize the exception.
Independent claims and dependent claims also fail to recite elements which amount to an improvement in the functioning of a computer or any other technology or technical field, apply the judicial exception with, or by use of, a particular machine, or apply or use the judicial exception in some other meaningful way beyond generally linking the use of the judicial exception to a particular technological environment, such that the claim as a whole is more than a drafting effort designed to monopolize the exception. For example, independent claims and dependent claims are directed to the abstract idea itself and do not amount to an integration according to any one of the considerations above.
Step 2B is the next step in the eligibility analyses and evaluates whether the claims recite additional elements that amount to an inventive concept (i.e., “significantly more”) than the recited judicial exception. According to Office procedure, revised Step 2A overlaps with Step 2B, and thus, many of the considerations need not be re-evaluated in Step 2B because the answer will be the same. See MPEP § 2106.
In Step 2A, several additional elements were identified as additional limitations:
A data processing system comprising: a processor, and a machine-readable storage medium storing executable instructions which, when executed by the processor, cause the processor alone or in combination with other processors to perform the following operations: … using a first artificial intelligence (AI) model … to a client device ...(Claim 1, 11, and 16);
…the first AI model is a customer usage machine learning model, and the machine-readable storage medium further includes instructions configured to cause the processor alone or in combination with other processors to perform operations of: training the customer usage machine learning model by labelling contextual features of the telemetry data and the cloud data associated with the customer, and inputting the labelled contextual features and training data to the customer usage machine learning model, (Claims 2, 12, and 17);
wherein the machine-readable storage medium further includes instructions configured to cause the processor alone or in combination with other processors to perform operations of:..(Claim 3, 5, 6, 8);
wherein the machine-readable storage medium further includes instructions configured to cause the processor alone or in combination with other processors to perform operations of: … using a second AI model… (Claim 4, 14, and 19);
wherein the machine-readable storage medium further includes instructions configured to cause the processor alone or in combination with other processors to perform operations of:… using the second AI model ...(Claim 7);
…using the AI model (Claim 10);
A non-transitory computer readable medium on which are stored instructions that, when executed, cause a programmable device to perform functions of:..(Claim 16).
These additional limitations, including the limitations in the independent claims and dependent claims, do not amount to an inventive concept because the recitations above do not amount to an improvement in the functioning of a computer or any other technology or technical field, apply the judicial exception with, or by use of, a particular machine, or apply or use the judicial exception in some other meaningful way beyond generally linking the use of the judicial exception to a particular technological environment, such that the claim as a whole is more than a drafting effort designed to monopolize the exception. In addition, they were already analyzed under Step 2A and did not amount to a practical application of the abstract idea.
For these reasons, the claims are rejected under 35 U.S.C. 101.
Claim Rejections - 35 USC § 103
In the event the determination of the status of the application as subject to AIA 35 U.S.C. 102 and 103 (or as subject to pre-AIA 35 U.S.C. 102 and 103) is incorrect, any correction of the statutory basis (i.e., changing from AIA to pre-AIA ) for the rejection will not be considered a new ground of rejection if the prior art relied upon, and the rationale supporting the rejection, would be the same under either status.
The following is a quotation of 35 U.S.C. 103 which forms the basis for all obviousness rejections set forth in this Office action:
A patent for a claimed invention may not be obtained, notwithstanding that the claimed invention is not identically disclosed as set forth in section 102, if the differences between the claimed invention and the prior art are such that the claimed invention as a whole would have been obvious before the effective filing date of the claimed invention to a person having ordinary skill in the art to which the claimed invention pertains. Patentability shall not be negated by the manner in which the invention was made.
Claim(s) 1-3, 5-6, 8-13, 15-18, and 20 are rejected under 35 U.S.C. 103 as being unpatentable over Scott et al. (US Pub. No. 20210306461 A1, hereinafter “Scott”) in view of McCoy et al. (US Pub. No. 20250217772 A1, hereinafter “McCoy”).
Regarding claims 1, 11, and 16
Scott discloses a data processing system comprising: a processor, and a machine-readable storage medium storing executable instructions which, when executed by the processor, cause the processor alone or in combination with other processors to perform the following operations (Scott, [0063]: computer-readable medium containing computer program code, which can be executed by a computer processor):
receiving a call requesting a first generative model to generate a partner recommendation for a customer of an entity (Scott, [0022]: receive call from user requesting to communicate with service provider; [0004]: machine learning used to determine which provider to direct the received request from user; [0030]: user employed by business);
… determine capability data associated with the partners based on … the historical execution metrics (Scott, [0032]: previous communication sessions from service provider that were positive used to determine the service provider is familiar with a certain profession and highly correlated);
providing as an input … the historical execution metrics to the first generative model and receiving as an output the capability data associated with the partners from the first generative model (Scott, [0031]: the vector associated with a service provider may include values representative of a service provider's services; [0032]: input provider information into machine learning model to output service provider most likely to best serve user’s needs based on previously reviewed communication sessions with service providers; [0033]: machine learning model trains feedback from previous communication sessions with service providers to strengthen or weaken association between service provider and user);
determining software usage data of the customer using a first artificial intelligence (AI) model based on telemetry data and cloud data associated with the customer; processing the software usage data of the customer and contextual data associated with the customer using a usage progression model to determine an optimal action or path for the customer (Scott, [0026]: monitors a user's actions on the client device and uses a machine learning model to determine that the combination of user actions is correlated to a service provider; [0004]: determine, using a machine learning model and encoded vectors representative of information from the activity history and user profile, to direct the received request to a payroll service provider; [0029]: activity history includes applications executed by client device, users actions in the applications, content user viewed or requested);
matching, via a match engine, the customer with one or more of the partners based on the software usage data of the customer, the optimal action or path, and the capability data associated with the partners (Scott, [0032]:Selects a service provider that is most likely to best serve the user's needs based on user activity history, user profile, reviews of previous communications with service providers; [0027]: train the machine learning model 230 and/or input service provider data stored in the service provider database 170 to select a service provider most likely to best serve a user; [0033]: feedback from previous sessions with service provider in combination with activity history is used to train the machine learning model; [0029]: activity history includes software usage data; [0026]: user's action);
and providing for display the matched one or more of the partners to a client device associated with the entity, the customer, or the matched one or more of the partners (Scott, [0052]: after determining that the accounting service provider is most likely to best serve the user, the enhanced routing system 101 initiates an instant messaging session between the client device of the user and a device of the accounting service provider; [0026]: transmit a notification to client device 101 prompting the user to accept a communication session invitation with a service provider; [0030]: user employed by business).
Scott does not teach:
constructing, via a prompt construction unit, a first prompt by appending to a first instruction string documentation submitted by partners of the entity and historical execution metrics associated with the partners, the first instruction string including instructions to the first generative model to determine capability data associated with the partners based on the documentation and the historical execution metrics; providing as an input the documentation …
However, McCoy teaches:
constructing, via a prompt construction unit, a first prompt by appending to a first instruction string documentation submitted by partners of the entity and historical execution metrics associated with the partners, the first instruction string including instructions to the first generative model to determine capability data associated with the partners based on the documentation …; providing as an input the documentation …(McCoy, [0028]: transform at least part of an output of a machine learning model into a transformed prompt having a format compatible with input to a generative AI model, and to transmit the transformed prompt to the generative AI model using data from external source (e.g. previous resumes, employment data); [0097]: provider institution computing system 102 to receive informal text descriptions, bullet point lists, and other data (e.g., example resumes, job titles, salary ranges) and generate a job posting; [0047]: parse the inputs (e.g., data, queries, strings of text, job descriptors, etc.) received from a user of the provider institution computing system 102 (e.g., a recruiter, an HR department, an entity, and the like) and determine/receive corresponding descriptive metrics representative of a job posting or potential candidates and convert text, images, resumes, lists, keywords, and the like into descriptive metrics such as job skills and experience levels that allow for further development of the job posting and then process the input and pull corresponding entity metrics 162 that may match, correlate, to, or satisfy the recruiter/user's preferences for the job posting).
It would have been obvious to one of ordinary skill in the art at the time the invention was made to have modified the capability data and historical execution metrics of Scott with constructing a prompt by appending documentation and historical execution metrics and determining the capability data based on documentation as taught by McCoy because the results of such a modification would be predictable. Specifically, Scott would continue to teach the capability data and historical execution metrics except that now constructing a prompt by appending documentation and historical execution metrics and determining the capability data based on documentation is taught according to the teachings of McCoy in order to improve curating datasets. This is a predictable result of the combination. (McCoy, [0002]).
Regarding claims 2, 12, and 17
The combination of Scott and McCoy teaches the data processing system of claim 1, wherein the first AI model is a customer usage machine learning model, and the machine-readable storage medium further includes instructions configured to cause the processor alone or in combination with other processors to perform operations of: training the customer usage machine learning model by labelling contextual features of the telemetry data and the cloud data associated with the customer, and inputting the labelled contextual features and training data to the customer usage machine learning model, and updating one or more weights associated with the one or more contextual features until reaching an accuracy level (Scott, [0031]: encoder may generate a mathematical representation that is of a smaller dimension (e.g., a smaller vector) of data from data curator for input to the machine learning model; [0032]: machine learning model receives input from encoder; [0029]: data curator maintains records of activity history of user of users actions in applications and webpages viewed; [0033]: adjust weights; [0056]: train machine learning model using encoded information).
Regarding claims 3, 13, and 18
The combination of Scott and McCoy teaches the data processing system of claim 1, wherein the machine-readable storage medium further includes instructions configured to cause the processor alone or in combination with other processors to perform operations of: training the usage progression model by labelling contextual features associated with historical software usage data of a plurality of customers in a plurality of industries, and inputting the labelled contextual features and training data to the usage progression model, and updating one or more weights associated with the one or more contextual features until reaching an accuracy level (Scott, [0031]: encoder may generate a mathematical representation that is of a smaller dimension (e.g., a smaller vector) of data from data curator for input to the machine learning model; [0032]: machine learning model receives input from encoder; [0029]: data curator maintains records of activity history of user including list of software applications executed; [0033]: adjust weights; [0056]: train machine learning model using encoded information).
Regarding claims 5, 15, and 20
The combination of Scott and McCoy teaches the data processing system of claim 4, wherein the machine-readable storage medium further includes instructions configured to cause the processor alone or in combination with other processors to perform operations of: …the matched one or more of the partners and the one or more existing relationships … the software usage data of the customer, the optimal action or path, and the capability data associated with the matched one or more partners…; the matched one or more of the partners based on the one or more existing relationships (Scott, [0032]:Selects a service provider that is most likely to best serve the user's needs; [0033]: feedback from previous sessions with service provider; [0029]: activity history includes software usage data; [0026]: user's action; [0030]: user profile includes information about a relationship between a user and a service provider that may include whether a user has an existing service account with the service provider).
Scott does not teach:
constructing, via the prompt construction unit, a second prompt by appending to a second instruction string…, the second instruction string including instructions to a second generative model to determine incentives for the customer to use the matched one or more of the partners…,…; and providing as an input the second prompt to the second generative model and receiving as an output the incentives from the second generative model.
However, McCoy teaches:
constructing, via the prompt construction unit, a second prompt by appending to a second instruction string…, the second instruction string including instructions to a second generative model to determine incentives for the customer to use the matched one or more of the partners…,…; and providing as an input the second prompt to the second generative model and receiving as an output the incentives from the second generative model (McCoy, [0028]: transform at least part of an output of a machine learning model into a transformed prompt having a format compatible with input to a generative AI model, and to transmit the transformed prompt to the generative AI model using data from external source (e.g. previous resumes, employment data); [0097]: provider institution computing system 102 to receive informal text descriptions and generate a job posting; [0047]: parse the inputs (e.g., data, queries, strings of text, job descriptors, etc.) received from a user of the provider institution computing system 102 (e.g., and determine/receive corresponding descriptive metrics; [0033]: metrics include benefits; [0077]: detailed explanation of benefits)
The motivation to combine Scott and McCoy is the same as set forth above in claim 1.
Regarding claim 6
The combination of Scott and McCoy teaches data processing system of claim 5, wherein the machine-readable storage medium further includes instructions configured to cause the processor alone or in combination with other processors to perform operations of: executing one or more nudging actions on the customer based on the incentives (McCoy, [0033]: metrics include benefits; [0077]: detailed explanation of benefits and information within metrics are tiered, ranked, or placed in hierarchy).
The motivation to combine Scott and McCoy is the same as set forth above in claim 1.
Regarding claim 8
The combination of Scott and McCoy teaches the data processing system of claim 7, wherein the machine-readable storage medium further includes instructions configured to cause the processor alone or in combination with other processors to perform operations of: detecting one or more partners having dominating priority scores; and randomly matching, via the match engine, the one or more partners having dominating priority scores with the plurality of customers (McCoy, [0052]: percent fit for each candidate matching user’s preferences and ranking list; [0073]: placing candidates with a higher score, percent match, or likelihood of success to propagate in an area of the user interface that is most visible; [0087]: minimum match threshold).
The motivation to combine Scott and McCoy is the same as set forth above in claim 1.
Regarding claim 9
The combination of Scott and McCoy teaches the data processing system of claim 1, wherein the documentation submitted by the partners include at least one of a statement of work, or a proof of execution (McCoy, [0097]: resume).
The motivation to combine Scott and McCoy is the same as set forth above in claim 1.
Regarding claim 10
The combination of Scott and McCoy teaches the data processing system of claim 1, wherein the software usage data of the customer is determined using the AI model further based on entity agent entry data associated with a customer relationship management system used by the entity (Scott, [0026]: monitors a user's actions on the client device and uses a machine learning model to determine that the combination of user actions is correlated to a service provider; [0004]: determine, using a machine learning model and encoded vectors representative of information from the activity history and user profile, to direct the received request to a payroll service provider; [0029]: activity history includes applications executed by client device, users actions in the applications, content user viewed or requested; [0030]: user employed by business and customer relationship management information; [0015]: human resources; claim 12: CRM data).
Claim(s) 4, 7, 14, and 19 are rejected under 35 U.S.C. 103 as being unpatentable over Scott and McCoy as applied to claim 1 above, and further in view of Davison et al. (US Pub. No. 20200012981 A1, hereinafter “Davison”).
Regarding claims 4, 14, and 19
The combination of Scott and McCoy teaches the data processing system of claim 1, wherein the machine-readable storage medium further includes instructions configured to cause the processor alone or in combination with other processors to perform operations of: determining one or more existing relationships between the customer and the partners …, wherein the matching is further based on the one or more existing relationships (Scott, [0016]: determine user profile; [0030]: user profile includes information about a relationship between a user and a service provider that may include whether a user has an existing service account with the service provider; [0032]:Selects a service provider that is most likely to best serve the user's needs based on user profile information input into machine learning model).
The combination of Scott and McCoy does not teach:
determining… using a second AI model based on a relationship graph database…
However, Davison teaches:
determining… using a second AI model based on a relationship graph database…(Davidson, [0067]: nodes in a graph database used to analyze data using machine learning).
It would have been obvious to one of ordinary skill in the art at the time the invention was made to have modified the determining step of Scott and McCoy with determining using a second AI model based on a relationship graph database as taught by Davison because the results of such a modification would be predictable. Specifically, Scott and McCoy would continue to teach determining except that now determining using a second AI model based on a relationship graph database is taught according to the teachings of Davison in order to sort data using graph database. This is a predictable result of the combination. (Davison, [0067]).
Regarding claim 7
The combination of Scott, McCoy, and Davison teaches the data processing system of claim 4, wherein the machine-readable storage medium further includes instructions configured to cause the processor alone or in combination with other processors to perform operations of: determining existing relationships among a plurality of customers and a plurality of partners…(Scott, [0016]: determine user profile; [0030]: user profile includes information about a relationship between a user and a service provider that may include whether a user has an existing service account with the service provider; [0032]:Selects a service provider that is most likely to best serve the user's needs based on user profile information input into machine learning model);
… an existing relationship in-between the customer and the partner…(Scott, [0030]: user profile includes information about a relationship between a user and a service provider that may include whether a user has an existing service account with the service provider).
Scott does not teach:
determining…using the second AI model based on the relationship graph database;
assigning a priority score for each pair of a customer and a partner in proportion with … relationship in-between the customer and the partner; matching, via the match engine, the plurality of customers with the plurality of partners based on the priority score.
However, McCoy teaches:
assigning a priority score for …a customer and a partner in proportion with … relationship …; matching, via the match engine, the plurality of customers with the plurality of partners based on the priority score (McCoy, [0051]: establish a hierarchy, score value, weighted value, or other relational comparison; [0052]: percent fit for each candidate matching user’s preferences and ranking list; [0074]: define or describe target candidates and scores matching; [0087]: narrow the applicant pool to ten high-scoring applicants).
The motivation to combine Scott and McCoy is the same as set forth above in claim 1.
However, Davison teaches:
assigning a priority score for each pair of…(emphasis added); determining…using the second AI model based on the relationship graph database (Davidson, [0067]: sum score for each pair and nodes in a graph database used to analyze data using machine learning).
The motivation to combine Scott, McCoy, and Davison is the same as set forth above in claim 4.
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure is cited as Makhija et al. (US Pub. No. 20250272652 A1) related to large language models-based data processing in one or more enterprise applications including procurement and supply chain applications, Puskarich et al. (US Pub. No. 20240422181 A1) related to using a graph database including nodes and relationship vectors, and non-patent literature, Artificial Intelligence Assisted Service Marketing Using Deep Assisted Neural Network, related to AI assisted customer support system to deliver experiences that result in advocates and consumers.
Any inquiry concerning this communication or earlier communications from the examiner should be directed to LATASHA DEVI RAMPHAL whose telephone number is (571)272-2644. The examiner can normally be reached 11 AM - 7:30 PM (EST).
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If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, Jeffrey A. Smith can be reached at 5712726763. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
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/LATASHA D RAMPHAL/Examiner, Art Unit 3688
/Jeffrey A. Smith/Supervisory Patent Examiner, Art Unit 3688
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