DETAILED ACTION
Status of Claims
The present application, filed on or after 3/16/2013, is being examined under the first inventor to file provisions of the AIA .
This action is in reply to the Remarks and Amendments received 03/25/2025.
Claims 11-20 remain withdrawn from further consideration pursuant to 37 CFR 1.142(b) as being drawn to a nonelected invention there being no allowable generic or linking claim. Election was made without traverse in the reply filed on 12/4/2023.
Claims 1 and 4 have been amended.
Claim 2 and 3 are canceled.
Claims 1 and 4-10 have been examined and are pending.
(AIA ) Examiner Note
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.
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 at the time any inventions covered therein were effectively filed 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 at the time a later invention was effectively filed 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
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.
Claim(s) 1, 4-10 are rejected under 35 U.S.C. 112(a) or 35 U.S.C. 112 (pre-AIA ), first paragraph, as failing to comply with the written description requirement. The 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 pre-AIA the inventor(s), at the time the application was filed, had possession of the claimed invention.
Independent claim 1 recites in part the following: “wherein the at least one processor further performs a computer-implemented method of training the neural network to determine the most relevant customer attributes to incorporate into the communication for success of the communication, from the set of known customer attributes, comprising: …
creating a first training set comprising the collected set of most relevant customer attributes, the modified set of most relevant customer attributes, and a set of not most relevant customer attributes; training the neural network in a first stage using the first training set…
creating a second training set for a second stage of training comprising the first training set and the set of not most relevant customer attributes that are incorrectly detected as most relevant customer attributes after the first stage of training; and training the neural network in the second stage using the second training set.”
Respectfully, although it is clear that the desired output, i.e. the desired result, from the claimed “training the neural network” is “the most relevant customer attributes to incorporate into the communication for success of the communication” and it is clear that at least two steps of this “training” involves some further steps of “training the neural network in a first stage using the first training set” and “training the neural network in the second stage using the second training set”, nonetheless, the original disclosure (specification, drawings, and original claims) fails to provide sufficient written description such that one skilled in the art can reasonably conclude that the inventor had possession of the claimed invention at the time of filing. Stated another way, the Specification lacks sufficient details such that a person of skill in the art would not understand, from a reading of the original disclosure, what steps are required to infringe applicant’s “training the neural network” to effectively achieve the claimed desired result of “the most relevant customer attributes to incorporate into the communication for success of the communication”.
First, applicant’s original disclosure fails to stipulate any particular structure of the claimed neural network. Instead, the Specification merely states, e.g. per [0077]: “an artificial intelligence (AI), such as a neural network, may be trained and provided with attributes of customer 218 to determine the most relevant attributes for the communication that is, or soon will be, underway.” And per [0088]: “A neural network, as is known in the art and in one embodiment, self-configures layers of logical nodes having an input and an output. If an output is below a self-determined threshold level, the output is omitted (i.e., the inputs are within the inactive response portion of a scale and provide no output), if the self-determined threshold level is above the threshold, an output is provided (i.e., the inputs are within the active response portion of a scale and provide an output), the particular placement of the active and inactive delineation is provided as a training step or steps. Multiple inputs into a node produce a multi-dimensional plane (e.g., hyperplane) to delineate a combination of inputs that are active or inactive.”
These aforementioned passages from the Specification are the most detail provided in the entire original disclosure regarding any supposed structure of the recited generic “neural network”. No further delineation of structure is found in the entire original disclosure. Therefore, no particular neural network is being claimed. The claims therefore are directed towards a generic neural network which is to be trained in two generic stages of training – note that details of these two supposed stages of training are also absent from the original disclosure. Therefore, the specification fails to provide sufficient written description to inform a person of ordinary skill in the art how the desired result is intended to be achieved by this generic neural network and its generic training stages regardless of the description of the input data (i.e. regardless of the “training set(s)” of data).
Second, the “first training set” is seen to encompasses all possible inputs – i.e. all possible customer attributes; i.e. it includes both: (1) a collected set of most relevant customer attributes, and (2) a set of not most relevant customer attributes, which in combination is effectively all customer attributes (both most relevant and not most relevant). The original disclosure does not elaborate upon this “first training set” as currently claimed. Therefore, the description of the claimed input cannot be used by a person of ordinary skill in the art to discern the steps necessary to train this generic neural network to produce the desired result as claimed. The combination of recited generic neural network and generic input which encompasses effectively all customer attributes (both most relevant and not most relevant) cannot be used by a person of ordinary skill in the art to discern either the steps necessary to train this generic neural network to achieve the desired result nor to discern any particular structure necessary for the claimed generic “neural network” which could be used to infer the steps necessary to implement “training the neural network in a first stage using the first training set”.
Lastly, a further description that this generic neural network is trained in a generic second stage using “the first training set” (which includes all customer attributes) as well as an undisclosed set of “not most relevant customer attributes that are incorrectly detected as most relevant customer attributes after the first stage of training” is not sufficient to inform a person of ordinary skill in the art how the desired result is intended to be achieved by undisclosed use of this extra set of training data; stated another way, there is no further description of how “training” is to be undertaken, instead only a desired result of “use” of this extra data in an undisclosed second stage of training of a generic neural network is provided.
The original disclosure (Specification, drawings, and original claims) therefore fails to address the level of detail required to satisfy the written description requirement. As noted supra, the Specification only generically describes use of a generic neural network and a desire that such generic neural network may be trained, via two generic training stages, to achieve a result as claimed of “to determine the most relevant customer attributes to incorporate into the communication for success of the communication, from the set of known customer attributes,” e.g. as noted per Specification [0077] “…server 210 determines the most relevant attribute(s) of customer 218. Attributes of customer 218 are nearly infinite… Accordingly, an artificial intelligence (AI), such as a neural network, may be trained and provided with attributes of customer 218 to determine the most relevant attributes for the communication that is, or soon will be, underway.”
A mere wish or plan from the inventors for obtaining the claimed invention does not satisfy the written description requirement. Boston Sci. Corp. v. Johnson & Johnson, 647 F.3d 1353, 1362 (Fed. Cir. 2011); Centocor Ortho Biotech, Inc. v. Abbott Labs., 636 F.3d 1341, 1348. (Fed. Cir. 2011). Notably, the "written description requirement is not met if the specification merely describes a 'desired result."' §112 Guidance at 61 (quoting Vasudevan Software, Inc. v. MicroStrategy, Inc., 782 F.3d 671,682 (Fed. Cir. 2015)).
In addressing the level of detail required to satisfy the written description requirement, the governing inquiry is one of fact, based on how one with ordinary skill in the art would have understood the patent specification, and is resolved on a case-by-case basis. Vas-Cath, Inc. v. Mahurkar, 935 F.2d 1555, 1563 (Fed. Cir. 1991). The level of detail required will vary depending on the nature and scope of the claims and on the complexity and predictability of the relevant technology. Ariad, 598 F.3d at 1351. Whether the disclosure is sufficient throughout its scope will depend on whether the specification describes only a portion of the claimed subject matter or instead describes the claimed subject matter more fully. For instance, a specification that describes only a result that one might achieve if one made the claimed subject matter is less likely to satisfy the written description requirement than a specification that describes in sufficient detail the claimed subject matter that is responsible for that result. Thus, a specification must demonstrate that one has conceived and described the breadth of the claimed subject matter as opposed to a mere research plan that invites others to explore the contours of the broadly claimed subject matter. See Ariad, 598 F.3d at 1353 (explaining that the written description requirement guards against claims that "merely recite a description of the problem to be solved while claiming all solutions to it and ... cover any compound later actually invented and determined to fall within the claim's functional boundaries"); Vasudevan, 7 82 F .3 d at 682 ("[The] written description requirement is not met if the specification merely describes a 'desired result."). Further, "while the description requirement does not demand any particular form of disclosure or that the specification recite the claimed invention in haec verba, a description that merely renders the invention obvious does not satisfy the requirement." Ariad, 598 F.3d at 1352 (citation omitted).
As such, the broadly recited limitations in question herein "merely recite[s] a description of the problem to be solved," and leaves to future inventors to "complete an unfinished invention." See Ariad, 598 F.3d at 1353. In this case, without the Specification describing any particular algorithm regarding “training” to achieve the claimed results, and without describing any particular neural network, one of skill in the art would not have reasonably concluded that the inventors invented the claimed invention or that they possessed the claimed subject matter at the time of filing of the application. See Vasudevan, 782 F.3d at 683; see also Regents, 119 F.3d at 1566; § 112 Guidance at 61.
Therefore, claim 1 is rejected as failing to comply with the written description requirement. Dependent claims 4-10 inherit the deficiency of parent claims from which they depend.
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, 4-10 are rejected under 35 U.S.C. 112(b) or (for pre-AIA ) 35 U.S.C. 112, second paragraph, as being indefinite for failing to particularly point out and distinctly claim the subject matter which the inventor, a joint inventor, or (for pre-AIA ) the applicant regards as the invention.
Independent claim 1 recites in part the following:
“…receiving, from the neural network, a subset of customer attributes as the most relevant customer attributes for the communication; … collecting a set of most relevant customer attributes from a database”; - underline added for emphasis by the Examiner.
First, the term “most relevant” is a relative term and is ambiguous; i.e. one person may deem “most relevant” to encompass one idea or a particular set of attributes and another person may deem this term to encompass a different idea or different set of attributes. Therefore, it is not clear what “set of most relevant customer attributes” must be received from a database to infringe the claims. For this reason, the claim is held to be indefinite.
Second, it is not clear whether the recited “a set of most relevant customer attributes” is intended to be the same or distinct from the previously referenced “the most relevant customer attributes”. For this reason, the claim is held to be indefinite.
Additionally, the claims recite in part the following: “…applying one or more transformations to each most relevant customer attribute of the set of most relevant customer attributes from the database, the transformations including deletion, adding an omitted customer attribute,… to create a modified set of most relevant customer attributes”
Respectfully, in the case where the only transformation applied to each “most relevant customer attribute” is deletion, there would not be any remaining attributes to be included in “a modified set”. Therefore, the claims encompass a situation where a created “modified set” is a null set, i.e. an empty set with no attributes. It is therefore not clear whether a modified set of most relevant attributes is actually necessary/required to infringe the invention. For this reason, the scope of what is intended to be claimed is not clear. For this reason, the claim is held to be indefinite.
Additionally, the claims have been amended to recite in part the following: “…creating a first training set comprising the collected set of most relevant customer attributes from the database,… and a set of not most relevant customer attributes”
Respectfully, in its broadest reasonable interpretation, this recited “first training set” is seen to encompasses all possible inputs – i.e. all possible customer attributes; i.e. it includes both: (1) a collected set of most relevant customer attributes, and (2) a set of not most relevant customer attributes, which in combination is effectively all customer attributes (both most relevant and not most relevant). Therefore, the scope of what is intended to be claimed appears to be infinite and not useful for training. This lack of utility for training raises questions of clarity; i.e. is applicant actually intending to claim a “first training set” includes effectively all customer attributes (both most relevant and not most relevant)? Because the limitation raises this question of utility and thus is unclear, the claim is indefinite.
Additionally, the claims have been amended to recite in part the following: “…creating a second training set for a second stage of training comprising the first training set and the set of not most relevant customer attributes that are incorrectly detected as most relevant customer attributes after the first stage of training…” – underline added for emphasis. Respectfully, the underlined feature lacks proper antecedent basis; i.e. no previous reference to “a” set of not most relevant customer attributes that are incorrectly detected as most relevant customer attributes has previously been recited. It appears that applicant is implying such a set is an outcome from the first training stage. However, this outcome has not previously been positively recited nor has any particular set of not most relevant customer attributes that are incorrectly detected as most relevant customer attributes previously been recited. At least because the feature lacks proper antecedent basis, the claims are considered indefinite.
Additionally, claim 4 has been amended to recite the following:
“The system of claim 1, wherein the at least one processor further performs a computer-implemented method of training the neural network to determine not most relevant customer attributes to exclude from the communication for the success of the
communication, from the set of known customer attributes, comprising:
creating a third training set comprising the collected set of not most relevant customer attributes, the modified set of not most relevant customer attributes, and a set of most
relevant customer attributes; training the neural network in a third stage using the first training set; creating a fourth training set for the third stage of training comprising
the first training set and the set of most relevant customer attributes that are incorrectly detected as not most relevant customer attributes after the first stage of training; and
training the neural network in the third stage using the second training set.”
Respectfully, although a “third training set” and “fourth training set” are now claimed as being “created”, these “third” and “fourth” training sets are not recited, nor claimed, as being used. The creation of these training sets are completely non-functional and non-used by the invention as claimed. Note that the training the neural network in a third stage is claimed as using the first training set. Similarly, the training the neural network in a fourth stage is claimed as using the second training set. There is no distinction, in the recited claims, between the “first stage” and “third stage” as they both train the neural net using the first training set. Similarly, there is no distinction, in the recited claims, between the “second stage” and “fourth stage” as they both train the neural net using the second training set.
Therefore, it is unclear what applicant is attempting to claim. For the purpose of compact prosecution, the features of claim 4 are interpreted to be commensurate with the training steps of claim 1 which respectively use the first training set and use the second training set.
Dependent claims 4-10 inherit the deficiencies of their parent claim and are also rejected under 35 U.S.C. 112(b) or (for pre-AIA ) 35 U.S.C. 112, second paragraph, as being indefinite for failing to particularly point out and distinctly claim the subject matter which the inventor, a joint inventor, or (for pre-AIA ) the applicant regards as the invention.
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 and 4-10 are rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea (i.e. a judicial exception) without significantly more.
Per step 1 of the 2019 Revised Patent Subject Matter Eligibility Guidance, the claims are directed towards a process, machine, or manufacture.
Per step 2A Prong One, the claims recite specific limitations which fall within at least one of the groupings of abstract ideas enumerated in the 2019 PEG, as follows:
Per Independent claim 1:
“…a computer-implemented method of training [a] neural network to determine the most relevant customer attributes to incorporate into [a] communication for success of the communication, from [a] set of known customer attributes, … receiving… a subset of customer attributes as the most relevant customer attributes for the communication…
encoding the subset of customer attributes as a cue comprising a presentation duration that does not exceed the duration of a ring signal when presented on an agent device to announce the communication;
signaling the agent device to present the cue to announce the communication and omitting the ring signal; and
upon determining the cue has been presented by the agent device, establishing the communication comprising connecting the agent device to a customer device via a communication network.”
As noted supra, these limitations fall within at least one of the groupings of abstract ideas enumerated in the 2019 PEG. Specifically, these limitations fall within the group Certain Methods Of Organizing Human Activity (e.g. 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).
That is, the step as drafted is a business decision use a generic neural network to determine relevant customer attributes and then to present, to a customer service agent, such customer attribute information deemed to be relevant to a pending conversation between the agent and a calling customer (e.g. to help get the conversation started on a good path for business) and thus falling into Certain Methods of Organizing Human Activity. There is no technical problem being solved and no technical solution for a technical problem. Note that no particular neural network model, no particular training technique, and no particular encoding method or technique is either contemplated or claimed. Furthermore, the mere nominal recitation of a generic processor does not take the claim limitation out of the enumerated grouping. Thus, the claims recite an abstract idea.
Per step 2A Prong 2, the Examiner finds that the judicial exception is not integrated into a practical application. Although there are additional elements, other than those noted supra, recited in the claims, none of these additional element(s) or a combination of elements as recited in the claims apply, rely on, or use the judicial exception in a manner that imposes a meaningful limit on the judicial exception, such that it is more than a drafting effort designed to monopolize the exception. As drafted, the claims as a whole merely describe how to generally “apply” the aforementioned concepts using generic computer infrastructure and link them to a field of use (i.e. preparing a customer service agent to interact with a customer for business purposes) or serve as insignificant extra-solution activity (e.g. data-gathering and transmittal). The claimed computer components are recited at a high level of generality and are merely invoked as tools to implement the idea but are not technical in nature. Simply implementing the abstract idea on or with generic computer components is not a practical application of the abstract idea. Furthermore, use of generic AI techniques such as generic neural networks operating upon generic customer attribute data to receive data deemed relevant is nothing more than insignificant pre-solution activity (e.g. data-gathering) when recited at this very high level of generality.
These additional limitations are as follows: “A system, comprising: at least one processor having instructions maintained in a non-transitory memory that cause the at least one processor to perform: providing a set of known customer attributes to a neural network trained to determine most relevant customer attributes for a communication comprising a customer and an agent; receiving, from the neural network, a subset of customer attributes… wherein the at least one processor further performs a computer-implemented method of training the neural network to determine the most relevant customer attributes to incorporate into the communication for success of the communication, from the set of known customer attributes, comprising: collecting a set of most relevant customer attributes from a database; applying one or more transformations to each most relevant customer attribute of the set of most relevant customer attributes from the database, the transformations including deletion, adding an omitted customer attribute, emphasizing one but less than all most relevant customer attributes, and deemphasizing at least one but less than all most relevant customer attributes to create a modified set of most relevant customer attributes; creating a first training set comprising the collected set of most relevant customer attributes from the database, the modified set of most relevant customer attributes, and a set of not most relevant customer attributes; training the neural network in a first stage using the first training set; creating a second training set for a second stage of training comprising the first training set and the set of not most relevant customer attributes that are incorrectly detected as most relevant customer attributes after the first stage of training; and training the neural network in the second stage using the second training set.”
However, these elements do not present a technical solution to a technical problem; i.e. Applicant’s invention is not a technique nor technical solution for “training” a neural network by “providing” input to the neural network model, or by “receiving” data from the neural network model, or by “collecting” data upon which insignificant pre-solution activity is performed (i.e. the applying one or more transformations” step) to prepare the data to input into the generic neural network model, nor is it a technical solution for “creating a first training set” and “second training set” (which appear to be nothing more than aggregation and data collection at this high level of generality). Applicant doesn’t recite any particular step of “training” except “using the first training step” and “using the second training set”. Therefore, these features merely serve to generally “apply” the aforementioned concepts using generic computer infrastructure and link them to a field of use or are insignificant extra-solution activity to the already identified abstract idea and do not integrate the abstract idea into a practical application thereof.
Per Step 2B, the Examiner does not find that the claims provide an inventive concept, i.e., the claims do not recite additional element(s) or a combination of elements that amount to significantly more than the judicial exception recited in the claim. As discussed with respect to Step 2A Prong Two, the additional elements in the independent claims were considered as merely serving to generally “apply” the aforementioned concepts via generically described computer components (e.g. by one or more processors) and “link” them to a field of use, or as insignificant extra-solution activity. For the same reason these elements are not sufficient to provide an inventive concept; i.e. the same analysis applies here in 2B. Mere instructions to apply an exception using a generic computer component and conventional data gathering cannot integrate a judicial exception into a practical application at Step 2A or provide an inventive concept in Step 2B. So, upon revaluating here in step 2B, these elements are determined to amount to no more than mere instructions to apply the exception using generic computer components (i.e. a server) and/or gather and transmit data which is well-understood, routine, conventional activity in the field; i.e. note the Symantec, TLI, and OIP Techs Court decisions cited in MPEP 2106.05(d)(ll) indicate that mere receipt or transmission of data over a network is a well-understood, routine, and conventional function when it is claimed in a merely generic manner (as it is here).
Accordingly, alone and in combination, these elements do not integrate the abstract idea into a practical application, as found supra, nor provide an inventive concept, and thus the claims are not patent eligible.
As for the dependent claims, the dependent claims do recite a combination of additional elements. However, these claims as a whole, considered either independently or in combination with the parent claims, do not integrate the identified abstract idea into a practical application thereof nor do they provide an inventive concept.
For example, dependent claim 6 recites the following: “wherein the duration of the ring signal is two seconds or less.” However, employing a ring which lasts a finite amount of time, and is indeed typical for ring signals in the telephony network, is part of the abstract idea but not significantly more than this idea. There is no technical solution presented nor claimed regarding improvements to any aspect core to the identified abstract idea.
Therefore, the Examiner does not find that these additional claim limitations integrate the abstract idea into a practical application nor provide an inventive concept. Instead, these limitations, as a whole and in combination with the already recited claim elements of the parent claims, are not significantly more than the already identified abstract idea. A similar finding is found for the remaining dependent claims.
For these reasons, the claims are not found to include additional elements that are sufficient to amount to significantly more than the judicial exception and are therefore patent ineligible.
Please see the 2019 Revised Patent Subject Matter Eligibility Guidance published in the Federal Register (84 FR 50) on January 7, 2019 (found at http://www.uspto.gov/patent/laws-and-regulations/examination-policy/examination-guidance-and-training-materials).
Claim Rejections - 35 USC § 103 (AIA )
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 non-obviousness.
Claims 1, 4, 5, 6, 8, 9, 10 are rejected under 35 U.S.C. 103 as obvious over Konig (U.S. 2017/0316438 A1; hereinafter, "Konig") in view of Ferguson et al. (US 2003/0130899 A1; hereinafter, "Ferguson"), Smith et al. (US 10,477,014 B1; hereinafter, "Smith") and Bleile (U.S. 6,044,148 A; hereinafter, "Bleile").
Claims 1 and 4: (currently amended)
Pertaining to claim 1 and 4, exemplified in the limitations of claim 1, Konig teaches the following:
A system, comprising: at least one processor having instructions maintained in a non-transitory memory that cause the at least one processor to perform:
providing a set of known customer attributes to a neural network trained to determine most relevant customer attributes for a communication comprising a customer and an agent; receiving, from the neural network, a subset of customer attributes as the most relevant customer attributes for the communication (Konig, see at least [0087]-[0091], e.g.: “…identify the customer as one who should receive proactive contact [a communication comprising an interaction between a customer and an agent] from an appropriate agent or knowledge worker of the organization to resolve the issue. These circumstances [subset of customer attributes] can be detected [received from a trained neural network] based on matching the current customer to customers in a similar situation who were upset (e.g., a predictor 220 may be a neural network trained to predict [determine] the probability of “customer dissatisfied” [most relevant customer attribute for the communication] based on a plurality of customer attributes [provided set of known customer attributes] such as “unusually high bill,” “previous unresolved contact regarding billing,” and “consistent on-time payment.”…”)
[…]
wherein the at least one processor further performs a computer-implemented method of training the neural network to determine the most relevant customer attributes to incorporate into the communication for success of the communication, from the set of known customer attributes (see 112(b) rejections guiding claim interpretations. Konig, see citations noted supra including again at least [0087]-[0091], teaching: “In some embodiments of the present invention, the predictors 220 are implemented using deep neural networks that are trained based on historical data… This [i.e. the neural network] may be trained based on identifying prior historical interactions with customers and using features or attributes (e.g., a set or vector of features or attributes) of the customers other than the customer's dissatisfaction as the inputs and whether or not the customer is dissatisfied as the output….”; see also at least [0114]-[0119]),
comprising:
collecting a set of most relevant customer attributes from a database (Konig, see again citations noted supra, e.g. at least [0087]-[0091] and [0114]-[0119], teaching, e.g.: “…trained based on historical data… identified by customer experience analytics system 47 (e.g., automatically identifying features that appear relevant…) [most relevant customer attributes from a database]…”)
applying one or more transformations to each most relevant customer attribute of the set of most relevant customer attributes from the database, the transformations including deletion, adding an omitted customer attribute, emphasizing one but less than all most relevant customer attributes, and deemphasizing at least one but less than all most relevant customer attributes to create a modified set of most relevant customer attributes (Konig, again citations already noted supra, e.g. [0087]-[0091] and [0114]-[0119], teaching, e.g.: “…for each example, the input portion of the training data is all of the features of the model except Ecustomer_call which is the output portion [most relevant customer attribute] or the label of the training data (i.e., for the given customer that is associated with the input features the historical truth of whether he called or not in the 48 hour period that occurred in the past. This is usually labeled as 1 for the event happened and 0 for had not happened) of the training data…”; applicant’s “adding an omitted customer attribute” reads on Konig’s addition of a “0” to represent customer event which did not happen; an event not occurring is an attribute for Konig’s neural network; Furthermore, per [0116]: “…present invention are not limited to predictors 220 using the above identified inputs and may exclude one or more of those inputs or may include additional inputs not specifically listed above. For example, the time periods may be varied and other parameters, such as whether the customer was recently contacted by the organization or whether the customer has an unresolved issue regarding a particular topic, may also be used as inputs to the predictor 220….”; applicant’s “deletion” reads on Konig’s exclude one or more of those inputs. Although Konig may not explicitly use the terminology emphasis or deemphasis of his inputs, he nonetheless provides motivation per his disclosure to try such emphasis and deemphasis of various inputs as he does explicitly state his method “may include additional inputs not specifically listed above. For example, the time periods may be varied…”; i.e. the relative importance of a particular time is emphasized or deemphasized as the time period of importance varies. Therefore, Examiner finds it would have been obvious to a person of ordinary skill in the art before the effective filing date of the claimed invention to have performed the limitation as claimed due to the motivation provided by Konig himself as noted supra because per MPEP 2143(I) (G) Some teaching, suggestion, or motivation in the prior art that would have led one of ordinary skill to modify the prior art reference teachings to arrive at the claimed invention is obvious. The motivation may be implicit and may be found in the knowledge of one of ordinary skill in the art, or, in some cases, from the nature of the problem to be solved. Id. at 1366, 80 USPQ2d at 1649.)
creating a first training set comprising the collected set of most relevant customer attributes from the database, the modified set of most relevant customer attributes, and a set of not most relevant customer attributes (Konig, again see citations noted supra, e.g. at least [0087], teaching: “…The training data [first training set from the database] is separated into a training set [collected set of most relevant customer attributes], a test set [modified set], and a validation set [a set of not most relevant customer attributes], as is well known in the art, and the back propagation algorithm may be used to train, in operation 346, a deep neural network (e.g., a neural network having an input layer, an output layer, and more than one hidden layer between the input layer and the output layer). The resulting trained neural network is a predictor for event Ecustomer_call (e.g., whether the given customer will call in the next 48 hours) and may then be output in operation 348 to be stored for later use in generating predictions….”)
training the neural network in a first stage using the first training set (Konig, see citations noted supra, e.g. at least [0087], teaching: “…the back propagation algorithm may be used to train, in operation 346, a deep neural network (e.g., a neural network having an input layer, an output layer, and more than one hidden layer between the input layer and the output layer). The resulting trained neural network is a predictor for event Ecustomer_call (e.g., whether the given customer will call in the next 48 hours) and may then be output in operation 348 to be stored for later use in generating predictions….”)
Although Konig teaches the above limitations, including training a neural network in a first stage using a first training set of data, Konig may not explicitly teach the nuance regarding a second stage of training of his neural network as recited below. However, regarding this feature, Konig in view of Ferguson teaches the following:
creating a second training set for a second stage of training comprising the first training set and the set of not most relevant customer attributes that are incorrectly detected as most relevant customer attributes after the first stage of training; and
training the neural network in the second stage using the second training set (Note 112(b) rejection guiding claim interpretation. Ferguson, see at least [0004]: “Examples of non-linear models may include neural networks and support vector machines (SVMs)…” and per [0037]-[0038], teaching e.g.: “…The non-linear model may then be trained [in a first stage] using the first training set. Then, a second training set may be generated by removing at least a subset of the parameter [relevant customer attributes] values [incorrectly detected as being relevant] of the first training set,…, and adding new parameter values [correctly detected as being not most relevant] from the training data based on the timestamps to generate a second training set... The non-linear model may then be trained [after the first stage of training] using the second training set.…”; applicant’s “the set of not most relevant customer attributes that are incorrectly detected as most relevant customer attributes“ reads on Ferguson’s identification of a subset of parameter values which are removed and replaced; i.e. the parameters are not removed but their values are removed and replaced with values appropriately indicating their relevance).
Therefore, the Examiner understands that the limitation in question is merely applying a known technique of Ferguson (directed towards techniques of training AI models using a first stage and second stage of training where the second stage uses a second training set of data based in part on the first set of training data such that at least a subset of the parameter [relevant customer attributes] values [incorrectly detected as being relevant] of the first training set are removed and replaced with new parameter values [correctly detected as being not most relevant] from the training data based on the timestamps to generate a second training set.) which is applicable to a known base device/method of Konig (already directed towards systems and methods of training a neural network using training data to predict customer behavior for a contact call center interaction between a customer and agent) to yield predictable results. Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to apply the techniques of Ferguson to the device/method of Konig in order to arrive at the limitation as claimed because Ferguson is pertinent to the neural network model training of Konig and because according to MPEP 2143(I) (C) and/or (D), the use of known technique to improve a known device, methods, or products in the same way (or which is ready for improvement) is obvious.
Although Konig/Ferguson teach the above limitations, and Konig teaches a customer analytics system which trains and uses neural networks to predict customer behavior for a contact call center, he may not explicitly teach the below recited nuance regarding use of his predicted customer behavior attributes as a cue. However, regarding these features, Konig in view of Smith teaches the following:
encoding the subset of customer attributes as a cue comprising a presentation duration that does not exceed the duration of a ring signal when presented on an agent device to announce the communication (Smith, see at least Figs. 5, 6, 7,8 and [6:35-8:10], e.g.: “While the CSR waits to connect with a caller, the application may display [signal the agent device to present] a waiting message 504 [the cue]… FIG. 6 illustrates collecting information [subset of customer attributes] for a CSR screen for a caller… The data for a caller may be obtained when the caller provides information sufficient to identify the caller. This information can be, for example, an account number, a customer or member number, a telephone number, an e-mail address, a government issued id number (such as a social security number), etc….”; Note that Smith’s message 504 [cue] which contains customer information [subset of customer attributes], is presented on CSR’s interface 708b per Fig. 7. And Smith teaches: “While the CSR waits to connect with a caller, the application may display a waiting message 504 [the cue]; therefore, the message 504 [cue] and selected customer information [subset of customer attributes] are able to be encoded and presented for a duration that does not exceed a ring signal – i.e. while the CSR waits to connect with the caller);
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signaling the agent device to present the cue to announce the communication […] (Smith, see again at least Figs. 5-8 and [6:35-8:10], e.g.: “While the CSR waits to connect with a caller, the application may display [signal the agent device to present] a waiting message 504 [the cue]… Fig. 6 illustrates collecting information for a CSR screen for a caller… When a CSR 702 has been selected to handle a call from a caller 606,…, the CSR may receive a notification from the dynamic ACD 614 notifying them that a call is available. When the CSR 702 accepts the call, the virtual machine 612 may share its virtual user interface 708a with the user interface 708b of the CSR 702, as represented by the two-headed arrow 704…”; Examiner notes Applicant’s intended reason for presenting the cue, i.e. to announce…, is non-functional descriptive material. Nonetheless, Smith’s message 504, e.g. presented on interface 708b, is also for the purpose of announcing information associated with pending call/communication.) […]; and
upon determining the cue has been presented by the agent device, establishing the communication comprising connecting the agent device to a customer device via a communication network (Smith, per at least Fig. 8 and again [6:35-8:10], teaches the CSR may be connected to the caller after the message has been shown to the CSR; e.g. steps 804 and 806 occur before step 808 “connecting the caller with the customer service representative”
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Therefore, the Examiner understands that the limitations in question are merely applying known techniques of Smith (directed towards a Customer service agent display receiving caller information prior to commencing communication with a caller, etc…) which are applicable to a known based device/method of Konig (already directed towards predicting relevant customer [caller] behavior attributes for such a contact call center and providing these to an agent before commencing communication with a caller) to yield predictable results. Therefore it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to apply the techniques of Smith to the device/method of Konig to improve Konig’s customer experience analytics system and because Smith and Konig are analogous art in the same field of endeavor (e.g. at least H04M 3/51 and/or G06Q 30/0204) and because according to MPEP 2143(I) (C) and/or (D), the use of known technique to improve a known device, methods, or products in the same way (or which is ready for improvement) is obvious.
Furthermore, although Konig in view of Smith teaches the above limitations upon which the following feature depends, and they already teach systems/methods directed towards a Customer service agent display receiving caller information prior to commencing communication with a caller, neither may explicitly teach the following nuance regarding omission of a ring signal as recited. However, regarding this feature, Konig/Smith in view of Bleile teaches the following:
and omitting the ring signal (Bleile, see at least Fig. 2b and [3:56-5:16], e.g.: “…It is also possible to use the present invention to receive and display data, without enabling [i.e. omitting] the ringer to sound, thereby providing a way of sending advertising or other information to a subscriber's telephone. In this scenario, the message may include an identification code to which the microprocessor responds by disabling [omitting] the ringer and displaying a data portion of the message on the display…”)
Therefore, the Examiner understands that the limitation in question is merely applying a known technique of Bleile (directed towards a technique of disabling [omitting] the ringer and displaying a data portion of the message on the display) which is applicable to a known based device/method of Konig/Smi