DETAILED ACTION
Notice of Pre-AIA or AIA Status
The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA .
Acknowledgments
Claims 1-30 are pending
Applicant provided information disclosure statement.
Allowable Subject Matter
Claims 2, 12, and 22 are allowable if rewritten to include all of the limitations of the base claim and any intervening claims, and if the independent claims were amended in such a way as to overcome the rejection(s) under 35 U.S.C. 101, set forth in this Office action. The closest prior art to these claims include Coccia (US20210103933A1) in further view of Lehr (US20190188774A1) in further view of Mahalanobish (US20220292407A1) who teaches a main machine learning model with different sub models as seen in para 0104. However, with respect to exemplary claims 2, 12, and 22 the closest prior art of record, either alone or taken in combination with any other references of record, do not anticipate or render obvious the claimed functionality of claims 2, 12, and 22.
Claims 4, 5, 14, 15, 24, and 25 are allowable if rewritten to include all of the limitations of the base claim and any intervening claims, and if the independent claims were amended in such a way as to overcome the rejection(s) under 35 U.S.C. 101, set forth in this Office action. The closest prior art to these claims include Coccia (US20210103933A1) in further view of Lehr (US20190188774A1) in further view of Doan (US11182719B1) who teaches a machine learning model in conjunction with work plan templates that are selected by the model. However, with respect to exemplary claims 4, 5, 14, 15, 24, and 25 the closest prior art of record, either alone or taken in combination with any other references of record, do not anticipate or render obvious the claimed functionality of claims 4, 5, 14, 15, 24, and 25.
Claims 6, 7, 16, 17, 26, and 27 are allowable if rewritten to include all of the limitations of the base claim and any intervening claims, and if the independent claims were amended in such a way as to overcome the rejection(s) under 35 U.S.C. 101, set forth in this Office action. The closest prior art to these claims include Coccia (US20210103933A1) in further view of Lehr (US20190188774A1) in further view of Gaur (US20210233164A1) who teaches a probability that a customer purchase will happen. However, with respect to exemplary claims 6, 7, 16, 17, 26, and 27 the closest prior art of record, either alone or taken in combination with any other references of record, do not anticipate or render obvious the claimed functionality of claims 6, 7, 16, 17, 26, and 27.
Claims 8, 18, and 28 are allowable if rewritten to include all of the limitations of the base claim and any intervening claims, and if the independent claims were amended in such a way as to overcome the rejection(s) under 35 U.S.C. 101, set forth in this Office action. The closest prior art to these claims include Coccia (US20210103933A1) in further view of Lehr (US20190188774A1) in further view of Haruta (US20180137526A1) who teaches churn rates of customers. However, with respect to exemplary claims 8, 18, and 28 the closest prior art of record, either alone or taken in combination with any other references of record, do not anticipate or render obvious the claimed functionality of claims 8, 18, and 28.
Claims 9, 19, and 29 are allowable if rewritten to include all of the limitations of the base claim and any intervening claims, and if the independent claims were amended in such a way as to overcome the rejection(s) under 35 U.S.C. 101, set forth in this Office action. The closest prior art to these claims include Coccia (US20210103933A1) in further view of Lehr (US20190188774A1) in further view of Bakulin (US20210042168A1) who teaches likelihood of sales increase or decrease. However, with respect to exemplary claims 9, 19, and 29 the closest prior art of record, either alone or taken in combination with any other references of record, do not anticipate or render obvious the claimed functionality of claims 9, 19, and 29.
Claims 10, 20, and 30 are allowable if rewritten to include all of the limitations of the base claim and any intervening claims, and if the independent claims were amended in such a way as to overcome the rejection(s) under 35 U.S.C. 101, set forth in this Office action. The closest prior art to these claims include Coccia (US20210103933A1) in further view of Lehr (US20190188774A1) in further view of Haruta (US20180137526A1) who teaches churn rates of customers in further view of Towriss (US20170161758A1) who teaches product configurations. However, with respect to exemplary claims 10, 20, and 30 the closest prior art of record, either alone or taken in combination with any other references of record, do not anticipate or render obvious the claimed functionality of claims 10, 20, and 30.
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 1-30 are rejected under 35 U.S.C. 101 because the claimed invention is directed to a judicial exception (i.e., a law of nature, a natural phenomenon, or an abstract idea) without significantly more than the judicial exception itself.
Regarding Step 1 of subject matter eligibility for whether the claims fall within a statutory category (See MPEP 2106.03), claims 1-30 are directed to non-transitory computer-readable medium, apparatus, and method.
Regarding step 2A-1, Claims 1-30 recite a Judicial Exception. Exemplary independent claim 1 and similarly claims 11 and 21 recite the limitations of
Executing… CRM functions…each…function associated with and configured to use one or more…models and one or more data models; administering…usage of the …models and the data models based on (i)…function of the multiple…functions being executed and (ii) a specified use case associated with the at least one… function being executed; generating evidence packages associated with predictions produced by the …models, each evidence package identifying features that contribute to the associated prediction generated by the…model; and providing one or more of the evidence packages as one or more inputs to at least one of the…models.
These limitations, as drafted, are a process that, under its broadest reasonable interpretation cover concepts of executing, administering, generating, and providing data. The claim limitations fall under the abstract idea grouping of mental process, because the limitations can be performed in the human mind, or by a human using a pen and paper. For example, but for the language of an apparatus and non-transitory computer-readable medium, the claim language encompasses simply executing functions with models, using the models, and generating/providing evidence packages. A user is able to execute a function such as carryout a task. A user is also able to utilize models to carry out a task. A user is also able to generate and provide data to a model such as an evidence package. These steps are mere data manipulation steps that do not require a computer.
The claims also recite CRM functions which deals with customer relationship management. The CRM functions relate to determining business variables such as gaps in revenue target and predicting transactions (See para 0041-0045 in Specifications). These make the claims fall in the abstract idea grouping of certain methods of organizing human activity (sales activity, fundamental economic principles or practices; business relations, interactions between people). It is clear the limitations recite these abstract idea groupings, but for the recitations of generic computer components. The mere nominal recitations of generic computer components does not take the limitations out of the mental process and certain methods of organizing human activity grouping. The claims are focused on the combination of these abstract idea processes.
Regarding step 2A-2- This judicial exception is not integrated into a practical application, and the claims do not include additional elements that are sufficient to amount to significantly more than the judicial exception.
The claims recite the additional elements of machine learning model, apparatus, processing device, non-transitory computer readable medium, processors, and model orchestrator.
These components are recited at a high level of generality, and merely automate the steps. Each of the additional limitations is no more than mere instructions to apply the exception using a generic computer component.
The combination of these additional elements is no more than mere instructions to apply the exception using a generic computer components or software. Accordingly, even in combination, these 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.
Further, the claims do not provide for recite any improvements to the functioning of a computer, or to any other technology or technical field; applying or using a judicial exception to effect a particular treatment or prophylaxis for a disease or medical condition; applying the judicial exception with, or by use of, a particular machine; effecting a transformation or reduction of a particular article to a different state or thing; or applying or using the judicial exception in some other meaningful way beyond generally linking the use of the judicial exception to a particular technological environment, such that the claim as a whole is more than a drafting effort designed to monopolize the exception.
The dependent claims have the same deficiencies as their parent claims as being directed towards an abstract idea, as the dependent claims merely narrow the scope of their parent claims. For example, the dependent claims further describe the additional element of machine learning models and the different types such as a core machine learning model. In addition, the dependent claims further describe what data the system used to make predictions such as internal and external data.
Regarding step 2B the claims do not include additional elements that are sufficient to amount to significantly more than the judicial exception because claim 1 recites
Method, however method is not considered an additional element.
Claim 1 further recites machine learning model and model orchestrator
Claim 11 recites apparatus, processing device, model orchestrator, and machine learning model
Claim 21 recites non-transitory computer readable medium, processors, and model orchestrator.
When looking at these additional elements individually, the additional elements are purely functional and generic the Applicant specification states general purpose computer configurations as seen in para 0108.
When looking at the additional elements in combination, the Applicant’s specification merely states a general-purpose computer configurations as seen in para 0108. The computer components add nothing that is not already present when the steps are considered separately. See MPEP 2106.05
Looking at these limitations as an ordered combination and individually adds nothing additional that is sufficient to amount to significantly more than the recited abstract idea because they simply provide instructions to use generic computer components, recitations of generic computer structure to perform generic computer functions that are used to "apply" the recited abstract idea. Thus, the elements of the claims, considered both individually and as an ordered combination, are not sufficient to ensure that the claim as a whole amounts to significantly more than the abstract idea itself.
Since there are no limitations in these claims that transform the exception into a patent eligible application such that these claims amount to significantly more than the exception itself, claims 1-30 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, 11, 13 and 21, 23 is/are rejected under 35 U.S.C. 103 as being unpatentable over Coccia (US20210103933A1) in further view of Lehr (US20190188774A1).
Regarding claims 1, and similarly claims 11 and 21, Coccia teaches
A method comprising (See para 0001-This application generally relates to customer relationship management systems. In particular, this application describes a method and system for guiding agent/customer interactions of a CRM system.) This teaches a method.
An apparatus comprising: at least one processing device configured to (See fig. 1) This shows a system with a processor. (See para 0013-The CRM system 105 may correspond to one or more computers that operate in a networked environment within an enterprise that collectively integrate and automate sales, marketing, and customer support. ) (See para 0015-The CGS 102 may include an input/output subsystem 110, an A1 subsystem 115, and a processor 125. The CGS 102 may include other subsystems)
A non-transitory computer readable medium storing computer readable program code that, when executed by one or more processors, causes the one or more processors to (See para 0039- In this regard, the operations may be implemented via instruction code stored in non-transitory computer readable media 127 that resides within the subsystems configured to cause the respective subsystems to perform the operations illustrated in the figures and discussed herein). This shows a memory.
executing at least one of multiple customer relationship management (CRM) functions using one or more processors (See figure 2) (See para 0010-The embodiments described below overcome the problems described above by providing a CRM guidance system that utilizes machine learning algorithms to monitor the current state of interaction between an agent and a customer and to predict a next best course of action for the agent to take in dealing with the customer. The system also utilizes machine learning algorithms to predict products that may be of interest to the customer.) This shows executing CRM functions such as determining the next best action and determining product of interest to the user.
each CRM function associated with and configured to use one or more trained machine learning models and one or more data models Machine learning algorithms correspond to machine learning models which carry out the functions as seen here (See para 0010-The embodiments described below overcome the problems described above by providing a CRM guidance system that utilizes machine learning algorithms to monitor the current state of interaction between an agent and a customer and to predict a next best course of action for the agent to take in dealing with the customer. The system also utilizes machine learning algorithms to predict products that may be of interest to the customer.) These models/algorithms are trained (See para 0023-For example, the analytics logic may include a machine learning model that is trained with information received from the datastore 106 of the CRM system 105.). The system also uses data models such as data structures in the datastores (See para 0014-In an embodiment, as least some of the information stored in the datastore(s) 106 may be communicated to the CGS 102. The information may be communicated in real-time or in batches).
administering, using a model orchestrator, usage of the machine learning models and the data models based on (i) the at least one CRM function of the multiple CRM functions being executed and (ii) a specified use case associated with the at least one CRM function being executed The system such as item 102 administers the machine learning models and uses the data models based on the function of next best action and products the customer is interested in. This is also with respect a special use case of the agent interacting with a customer and needs help handling the phone call as seen in fig. 4. Item 102 functions as the model orchestrator since it uses the machine learning models and data models to carry out the functions.
Even though Coccia teaches machine learning models it doesn’t teach evidence packages, however Lehr teaches
generating evidence packages associated with predictions produced by the machine learning models, each evidence package identifying features that contribute to the associated prediction generated by the associated machine learning model (See para 0015- The evidence package is a collection of various exploration results ‘saved’ by the user while exploring diverse data sources to identify potential root cause of an issue. Similarly, various filters and selection of assets may be added to the evidence package… The evidence package includes micro-level metadata corresponding to tracking the exploration performed by the individual users, search terms provided as input for analysis, the list of micro services selected, etc. The evidence package including the micro-level metadata represents the user behavior, and is provided as input to a machine learning algorithm… Subsequently used when a different user tries to perform the same or similar exploration, the predicted list of recommended micro services is provided as output to the user.) This shows that the system generates evidence packages since data is added to them by the system. These are associated with the machine learning algorithms/models since the machine learning algorithms/models uses the evidence packages to make predictions of which micro services the user wants. The evidence packages has features such as microlevel metadata that contribute to the prediction.
and providing one or more of the evidence packages as one or more inputs to at least one of the machine learning models (See para 0015- The evidence package includes micro-level metadata corresponding to tracking the exploration performed by the individual users, search terms provided as input for analysis, the list of micro services selected, etc. The evidence package including the micro-level metadata represents the user behavior, and is provided as input to a machine learning algorithm. The machine learning algorithm receives the evidence packages as input, performs analysis and learns the user behavior and predicts a list of recommended micro service as output.) This shows that evidence packages are used as input to the machine learning algorithms/models.
Coccia and Lehr are analogous art because they are from the same problem-solving area of machine learning models and making predictions and both belong to G06Q30 classification. It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to have combined Coccia’s invention by incorporating the method of Lehr because Coccia would also be able to use evidence packages when determining what the next best action would be concerning the customer. The evidence package would provide a convenience to the user of Coccia since it would gather all relevant information regarding the customer in addition to just looking at data store information. Evidence packages also make machine learning models more accurate and more efficient.
Regarding claim 3, 13, and 23, Coccia further teaches
wherein one or more of the machine learning models are configured to generate the predictions using (i) internal information of a company seeking to provide one or more products or services to customers and (ii) external information from outside the company. The machine learning model uses internal information such as marketing efforts and past sales and external information such as customer information. (See para 0013- For example, the dashboards may provide customer biographic information, past sales, previous marketing efforts, and more, summarizing all of the relationships between the customer and the company. In this regard, the CRM system 105 may include one or more datastores 106 for storing this information.)
Conclusion
The prior art made of record and not relied upon considered pertinent to Applicant’s disclosure.
Mahalanobish (US20220292407A1) who teaches a main machine learning model with different sub models as seen in para 0104.
Doan (US11182719B1) who teaches a machine learning model in conjunction with work plan templates that are selected by the model.
Gaur (US20210233164A1) who teaches a probability that a customer purchase will happen.
Haruta (US20180137526A1) who teaches churn rates of customers.
Bakulin (US20210042168A1) who teaches likelihood of sales increase or decrease.
Towriss (US20170161758A1) who teaches product configurations.
Any inquiry concerning this communication or earlier communications from the examiner should be directed to MUSTAFA IQBAL whose telephone number is (469)295-9241. The examiner can normally be reached Monday Thru Friday 9:30am-7:30 CST.
Examiner interviews are available via telephone, in-person, and video conferencing using a USPTO supplied web-based collaboration tool. To schedule an interview, applicant is encouraged to use the USPTO Automated Interview Request (AIR) at http://www.uspto.gov/interviewpractice.
If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, Beth Boswell can be reached at (571) 272-6737. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
Information regarding the status of published or unpublished applications may be obtained from Patent Center. Unpublished application information in Patent Center is available to registered users. To file and manage patent submissions in Patent Center, visit: https://patentcenter.uspto.gov. Visit https://www.uspto.gov/patents/apply/patent-center for more information about Patent Center and https://www.uspto.gov/patents/docx for information about filing in DOCX format. For additional questions, contact the Electronic Business Center (EBC) at 866-217-9197 (toll-free). If you would like assistance from a USPTO Customer Service Representative, call 800-786-9199 (IN USA OR CANADA) or 571-272-1000.
/MUSTAFA IQBAL/Primary Examiner, Art Unit 3625