DETAILED ACTION
Claims 1-20 are pending. Claims 1-20 are considered in this Office 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 .
Double Patenting
The nonstatutory double patenting rejection is based on a judicially created doctrine grounded in public policy (a policy reflected in the statute) so as to prevent the unjustified or improper timewise extension of the “right to exclude” granted by a patent and to prevent possible harassment by multiple assignees. A nonstatutory double patenting rejection is appropriate where the claims at issue are not identical, but at least one examined application claim is not patentably distinct from the reference claim(s) because the examined application claim is either anticipated by, or would have been obvious over, the reference claim(s). See, e.g., In re Berg, 140 F.3d 1428, 46 USPQ2d 1226 (Fed. Cir. 1998); In re Goodman, 11 F.3d 1046, 29 USPQ2d 2010 (Fed. Cir. 1993); In re Longi, 759 F.2d 887, 225 USPQ 645 (Fed. Cir. 1985); In re Van Ornum, 686 F.2d 937, 214 USPQ 761 (CCPA 1982); In re Vogel, 422 F.2d 438, 164 USPQ 619 (CCPA 1970); and In re Thorington, 418 F.2d 528, 163 USPQ 644 (CCPA 1969).
A timely filed terminal disclaimer in compliance with 37 CFR 1.321(c) or 1.321(d) may be used to overcome an actual or provisional rejection based on a nonstatutory double patenting ground provided the reference application or patent either is shown to be commonly owned with this application, or claims an invention made as a result of activities undertaken within the scope of a joint research agreement. A terminal disclaimer must be signed in compliance with 37 CFR 1.321(b).
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Claim 1 of the current application (Hereby known as ‘923) is rejected on the grounds of nonstatutory double patenting as being unpatentable over claim 1 of U.S. Patent No. 12,282,882 (Hereby known as ‘882). Although the claims at issue are not identical, they are not patentably distinct from each other because:
Regarding Claims 1, 11, and 20, Claims 1, 11, and 20 of the current application (‘923) recite substantially similar steps of '822 – Claim 1.
Claims 1, 11, and 20 of ‘923 recites the steps of:
A computer-implemented method comprising:
identifying a provider in a provider network associated with provisioning a specific product to a specific user;
accessing context information related to the provider provisioning one or more products in relation to one or more workflows for provisioning the one or more products through the provider network;
processing the context information using a trained machine learning model, wherein the trained machine learning model generates an output comprising circumstances associated with a likelihood that the provider will contact an enterprise of the provider network with respect to provisioning of the specific product to the specific user; and
facilitating, based on the circumstances associated with the likelihood that the provider will contact the enterprise of the provider network, performance of one or more remedial actions through the enterprise to address the provider contacting the enterprise.
Whereas Claim 1 of ‘822 states:
A computer-implemented method comprising:
identifying a provider in a provider network associated with provisioning a specific product to a specific user;
accessing context information related to the provider provisioning one or more products in relation to one or more workflows for provisioning the one or more products through the provider network;
processing the context information using a trained machine learning model, wherein the trained machine learning model generates an output comprising circumstances associated with a likelihood that the provider will contact an enterprise of the provider network with respect to provisioning of the specific product to the specific user;
facilitating, based on the circumstances associated with the likelihood that the provider will contact the enterprise of the provider network, performance of one or more remedial actions through the enterprise to address the provider contacting the enterprise; and
updating training of the trained machine learning model using the context information, the output of the trained machine learning model, and additional context information associated with a response to the one or more remedial actions.
These are obvious variants of each other as both recite substantially the same limitations. Further, elimination of an element or its functions is deemed to be obvious in light of prior art teachings of at least the recited element or its functions (see In re Karlson, 136 USPQ 184, 186; 311 F2d 581 (CCPA 1963)), thereby rendering the elimination of any elements recited in the claims of the related patent (that are not recited in the instant claims) obvious.
Thus, Claims 1, 11, and 20 of the current application are obvious variants of claim 1 in ‘822.
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.
Alice – Claims 1-20 are rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more.
Claims 1, 11, and 20 are directed to the limitations for identifying a provider in a provider network associated with provisioning a specific product to a specific user (Receiving and Analyzing Information, an observation and evaluation, a Mental Process; Managing Human Activity, i.e. managing providers/customers; a Certain Method of Organizing Human Activity); accessing context information related to the provider provisioning one or more products in relation to one or more workflows for provisioning the one or more products through the provider network (Receiving and Analyzing the Information, an observation and evaluation, a Mental Process; Managing Human Activity, i.e. managing providers/customers; a Certain Method of Organizing Human Activity); processing the context information which generates an output comprising circumstances associated with a likelihood that the provider will contact an enterprise of the provider network with respect to provisioning of the specific product to the user (Analyzing the Information, an evaluation, a Mental Process; Managing Human Activity, i.e. managing providers/customers; a Certain Method of Organizing Human Activity); and facilitating, based on the circumstances associated with the likelihood that the provider will contact the enterprise of the provider network, performance of one or more remedial actions through the enterprise to address the provider contacting the enterprise (Transmitting the Analyzed Information, a judgment, a Mental Process; Managing Human Activity, i.e. managing providers/customers; a Certain Method of Organizing Human Activity), which under their broadest reasonable interpretation, covers performance of the limitation in the mind for the purposes of making a decision using a prediction, but for the recitation of generic computer components. That is, other than reciting a computer system, one or more processors, a trained machine learning model, and a computer-readable storage medium, nothing in the claim element precludes the step from practically being performed or read into the mind for the purposes of Managing Human Activity, i.e. managing providers/customers. For example, facilitating, based on the circumstances associated with the likelihood that the provider will contact the enterprise of the provider network, performance of one or more remedial actions through the enterprise to address the provider contacting the enter encompasses a manager or supervisor identifying that a client will probably contact them or a superior due to an issue, and facilitating some sort of action based on that determination/identification, an observation, evaluation, and judgment. If a claim limitation, under its broadest reasonable interpretation, covers performance of the limitation in the mind but for the recitation of generic computer components, then it falls within the “Mental Processes” grouping of abstract ideas, an observation, evaluation, and judgment. Further, as described above, the claims recite limitations for Managing Human Activity, a “Certain Method of Organizing Human Activity”. Accordingly, the claim recites an abstract idea.
This judicial exception is not integrated into a practical application. In particular, the claim recites the above stated additional elements to perform the abstract limitations as above. The computer readable medium, system, trained machine learning model, and processors are recited at a high-level of generality (i.e., as a generic software/module performing a generic computer function of storing, retrieving, sending, and processing data) such that they amount to no more than mere instructions to apply the exception using generic computer components. Even if taken as an additional element, the receiving and transmission steps above are insignificant extra-solution activity as these are receiving, storing, and transmitting data as per the MPEP 2106.05(d). Accordingly, 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. The claim is directed to an abstract idea.
The claim does not include additional elements that are sufficient to amount to significantly more than the judicial exception, when considered both individually and as an ordered combination. As discussed above with respect to integration of the abstract idea into a practical application, the additional element being used to perform the abstract limitations stated above amount to no more than mere instructions to apply the exception using generic computer components. Mere instructions to apply an exception using generic computer components cannot provide an inventive concept. The claim is not patent eligible. Applicant’s Specification states:
“[0109] Devices implementing methods according to these disclosures can comprise hardware, firmware and/or software, and can take any of a variety of form factors. Typical examples of such form factors include laptops, smart phones, small form factor personal computers, personal digital assistants, rackmount devices,”
Which states that any type of computer can be used, such as any personal computer, laptop, mobile phone, tablet, etc., to perform the abstract limitations, and from this interpretation, one would reasonably deduce the aforementioned steps are all functions that can be done on generic components, and thus application of an abstract idea on a generic computer, as per the Alice decision and not requiring further analysis under Berkheimer, but for edification the Applicant’s specification has been used as above satisfying any such requirement. This is “Applying It” by utilizing current technologies. For the receiving and transmitting steps that were considered extra-solution activity in Step 2A above, if they were to be considered additional elements, they have been re-evaluated in Step 2B and determined to be well-understood, routine, conventional, activity in the field. The background does not provide any indication that the additional elements, such as the system, software, analyzers, etc., nor the receiving and transmitting steps as above, are anything other than a generic, and the MPEP Section 2106.05(d) indicates that mere collection or receipt, storing, or transmission of data is a well‐understood, routine, and conventional function when it is claimed in a merely generic manner (as it is here). For these reasons, there is no inventive concept. The claim is not patent eligible.
Claims 2-10 and 12-19 contain the identified abstract ideas, further narrowing them, with no additional elements to be considered as part of a practical application or under prong 2 of the Alice Analysis of the MPEP, thus not integrated into a practical application, nor are they significantly more for the same reasons and rationale as above.
After considering all claim elements, both individually and in combination, Examiner has determined that the claims are directed to the above abstract ideas and do not amount to significantly more. Therefore, the claims and dependent claims are rejected under 35 U.S.C. 101 as being directed to non-statutory subject matter. See Alice Corporation Pty. Ltd. v. CLS Bank International, No. 13–298.
Claim Rejections - 35 USC § 103
The following is a quotation of 35 U.S.C. 103 which forms the basis for all obviousness rejections set forth in this Office action:
A patent for a claimed invention may not be obtained, notwithstanding that the claimed invention is not identically disclosed as set forth in section 102 of this title, if the differences between the claimed invention and the prior art are such that the claimed invention as a whole would have been obvious before the effective filing date of the claimed invention to a person having ordinary skill in the art to which the claimed invention pertains. Patentability shall not be negated by the manner in which the invention was made.
The factual inquiries set forth in Graham v. John Deere Co., 383 U.S. 1, 148 USPQ 459 (1966), that are applied for establishing a background for determining obviousness under 35 U.S.C. 103 are summarized as follows:
1. Determining the scope and contents of the prior art.
2. Ascertaining the differences between the prior art and the claims at issue.
3. Resolving the level of ordinary skill in the pertinent art.
4. Considering objective evidence present in the application indicating obviousness or nonobviousness.
Claims 1-20 are rejected under 35 U.S.C. 103 as being unpatentable over Hollenburg (U.S. Publication No. 2016/003,6981) in view of Nunes (U.S. Publication No. 2022/011,4594).
Regarding Claims 1, 11, and 20, Hollenburg, a system and method for case-based routing for a contact, teaches a computer-implemented method comprising:
identifying a provider in a provider network associated with provisioning a specific product to a specific user ([0096] the product and provider are determined as well as information about the customer);
accessing context information related to the provider provisioning one or more products in relation to one or more workflows for provisioning the one or more products through the provider network ([0038] information is accessed for an anticipated workflow which has to do with the sales of service and products as in [0041]); and
facilitating, based on the circumstances associated with the model and product of the user ([0094-96] as above), performance of one or more remedial actions through the enterprise to address the provider contacting the enterprise ([0155] remedial actions such as training are taken for the provider of the product).
Although Hollenburg teaches use of a model for contact of support for a product with respect to provisioning of the specific product to the specific user as in [0094-96], it does not explicitly state a propenisity or likelihood that a provider will contact an enterprise or company.
Nunes, an analysis platform for actionable insight into user interaction data, teaches applying a model based on the context information to identify circumstances associated with a likelihood that the provider will contact an enterprise of the provider network with respect to provisioning of the specific product to the specific user ([0104-106] an algorithm and model is used to determine the likelihood score of events happening in a provider network and this [0028] model is a trained machine learning model); and
It would be obvious to one of ordinary skill in the art at the time the claimed invention was filed to combine the model used for contacting agents in Hollenberg with the models and algorithms for determining a likelihood of outcomes of Nunes as they are both analogous art along with the claimed invention which all teach solutions to contacting customers and routing of calls, and the combination would leave to an improved system which would decrease the noise activity in the algorithms and thus increase the efficiency and accuracy of the system as taught in [0081] of Nunes.
Examiner notes Hollenberg teaches a computer-readable medium, processors, and system ([0190] an apparatus with processor [0193] and medium [0190]).
Regarding Claims 2 and 12, Hollenburg teaches wherein the circumstances associated with the likelihood that the provider will contact the enterprise of the provider network include at least one of:
one or more temporal probabilities that the provider will contact the enterprise of the provider network; or one or more subjects that the provider might address when contacting the enterprise, wherein the one or more subjects are identified by applying the trained machine learning model to the context information (Hollenberg teach this is Claims 1 and 3 above, and that a subject is determined specific to the customer/product as in [0112]).
Although Hollenberg teaches a model and contacting the customer as in Claim 1 above, it does not explicitly state using probabilities to do so.
Nunes teaches
one or more probabilities that the provider will contact the enterprise of the provider network ([0097] probabilities are used in likelihood prediction).
It would be obvious to one of ordinary skill in the art at the time the claimed invention was filed to combine the model used for contacting agents in Hollenberg with the models and algorithms for determining a likelihood of outcomes of Nunes as they are both analogous art along with the claimed invention which all teach solutions to contacting customers and routing of calls, and the combination would leave to an improved system which would decrease the noise activity in the algorithms and thus increase the efficiency and accuracy of the system as taught in [0081] of Nunes.
Regarding Claims 3 and 13, Hollenburg teaches wherein the one or more subjects are specific to provisioning of the specific product to the specific user ([0112] subject is specific to the customer/product).
Regarding Claims 4 and 14, Hollenburg teaches wherein the one or more products include the specific product provisioned to the specific user and the context information includes characteristics associated with the provider provisioning the specific product to the specific user in relation to the one or more workflows (Hollenberg uses characteristics and attributes in determine what type of service and how to contact customers as in [0085] and [0094]).
Regarding Claims 5 and 15, Hollenburg teaches wherein the context information includes at least one of:
temporal context information that is representative of characteristics at different times during the one or more workflows; or characteristics associated with the provider previously provisioning a product to a user in relation to the one or more workflows ([0162-163] the workflow and steps are determined based on the particular agent and action required).
Regarding Claims 6 and 16, Hollenburg teaches further comprising:
facilitating performance of a remedial action of the one or more remedial actions through the enterprise before the provider contacts the enterprise ([0155] remedial actions such as training are taken for the provider of the product).
Regarding Claims 7 and 17, Hollenburg teaches further comprising:
selecting a remedial action of the one or more remedial actions to perform based on the circumstances associated with the likelihood that the provider will contact the enterprise of the provider network ([0140-141] when an agent is selected it is done so with their circumstances and selection in mind); and
facilitating performance of the remedial action that is selected based on the circumstances associated with the likelihood that the provider will contact the enterprise of the provider network ([0155] remedial actions such as training are taken for the provider of the product).
Although Hollenberg teaches the model and contacting the customer as in Claim 1 above, it does not explicitly state a likelihood.
Nunes teaches wherein the circumstances associated with the likelihood that the provider will contact the enterprise of the provider network include one or more probabilities that the provider will contact the enterprise of the provider network ([0097] probabilities are used in likelihood prediction).
It would be obvious to one of ordinary skill in the art at the time the claimed invention was filed to combine the model used for contacting agents in Hollenberg with the models and algorithms for determining a likelihood of outcomes of Nunes as they are both analogous art along with the claimed invention which all teach solutions to contacting customers and routing of calls, and the combination would leave to an improved system which would decrease the noise activity in the algorithms and thus increase the efficiency and accuracy of the system as taught in [0081] of Nunes.
Regarding Claims 8 and 18, teaches use of a threshold in determination of a remedial action for contacting an employee such as in [0102], and
one or more subjects that the provider might address when contacting the enterprise as included as part of the circumstances associated with the likelihood that the provider will contact the enterprise of the provider network ([0087-89] the circumstance/factors are used to adjust the likelihood of proactive contact), but it does not explicitly teach a probability.
Although Hollenberg teaches the model and contacting the customer as in Claim 1 above, it does not explicitly state a likelihood.
Nunes teaches wherein the circumstances associated with the likelihood that the provider will contact the enterprise of the provider network include one or more probabilities that the provider will contact the enterprise of the provider network ([0097] probabilities are used in likelihood prediction).
It would be obvious to one of ordinary skill in the art at the time the claimed invention was filed to combine the model used for contacting agents in Hollenberg with the models and algorithms for determining a likelihood of outcomes of Nunes as they are both analogous art along with the claimed invention which all teach solutions to contacting customers and routing of calls, and the combination would leave to an improved system which would decrease the noise activity in the algorithms and thus increase the efficiency and accuracy of the system as taught in [0081] of Nunes.
Regarding Claim 9, Hollenberg teaches use of a threshold in determination of a remedial action for contacting an employee such as in [0102], it does not explicitly teach a probability.
Although Hollenberg teaches the model and contacting the customer as in Claim 1 above, it does not explicitly state a likelihood.
Nunes teaches wherein the circumstances associated with the likelihood that the provider will contact the enterprise of the provider network include one or more probabilities that the provider will contact the enterprise of the provider network ([0097] probabilities are used in likelihood prediction).
It would be obvious to one of ordinary skill in the art at the time the claimed invention was filed to combine the model used for contacting agents in Hollenberg with the models and algorithms for determining a likelihood of outcomes of Nunes as they are both analogous art along with the claimed invention which all teach solutions to contacting customers and routing of calls, and the combination would leave to an improved system which would decrease the noise activity in the algorithms and thus increase the efficiency and accuracy of the system as taught in [0081] of Nunes.
Regarding Claim 10, Hollenburg teaches The computer-implemented method of claim 7, wherein the remedial action includes instructing a contact representative of the enterprise of the provider network to contact the provider irrespective of the provider actually contacting the enterprise of the provider network ([0005] the system allows for proactively contacting the customer).
Regarding Claim 19, Claim 19 is taught for the same reasons and rationale as in Claims 9 and 10 above.
Conclusion
The prior art made of record is considered pertinent to applicant's disclosure.
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/JOSEPH M WAESCO/Primary Examiner, Art Unit 3683 6/8/2026
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