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 .
Notice to Applicant
Claims 1- 20 have been examined in this application. This communication is the first action on the merits. Information Disclosure Statement (IDS) filed 11/6/2024 is acknowledged.
Claim Rejections - 35 USC § 101
35 U.S.C. 101 reads as follows:
Whoever invents or discovers any new and useful process, machine, manufacture, or composition of matter, or any new and useful improvement thereof, may obtain a patent therefor, subject to the conditions and requirements of this title.
Claims 1- 20 are rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more. Claims 1-20 are directed to predicting customer interaction using deep learning.
Claim 1 recites a system for predicting customer interaction using deep learning, Claim 12 recites a method for predicting customer interaction using deep learning and Claim 20 recites an article of manufacture for predicting customer interaction using deep learning, which include retrieving one or more sets of emotion factor values for communication data of one or more corresponding customer communications associated with a customer over time, wherein each emotion factor value of a given set of emotion factor values indicates a measure of a different emotion in a corresponding customer communication, and wherein the given set of emotion factor values comprises a determination value for the corresponding customer communication, an inquisitiveness value for the corresponding customer communication, a valence value for the corresponding customer communication, and an aggression value for the corresponding customer communication; classifying using an emotion propensity model the customer into an emotional profile according to a typical emotional response of the customer based on the one or more sets of emotion factor values for the communication data of the one or more corresponding customer communications associated with the customer over time; determining a probability that the customer will respond positively to a particular type of customer engagement based on the emotional profile for the customer.
As drafted, this is, under its broadest reasonable interpretation, within the Abstract idea grouping of “Methods of Organizing Human Activity” – managing personal behavior. The recitation of “computing system”, “memory”, “processor”, “database”, and “computer readable medium”, provide nothing in the claim elements to preclude the step from being “Methods of Organizing Human Activity”- managing personal behavior. Accordingly, the claim recites an abstract idea.
This judicial exception is not integrated into a practical application. The claims primarily recite the additional element of using computer components to perform each step. The “computing system”, “memory”, “processor”, “database”, and “computer readable medium” is recited at a high-level of generality, such that it amounts no more than mere instructions to apply the exception using a computer component. See MPEP 2106.05(f). Accordingly, the additional elements do not integrate the abstract idea into a practical application because it does not impose any meaningful limits on practicing the abstract idea. The claims also fail to recite any improvements to another technology or technical field, improvements to the functioning of the computer itself, use of a particular machine, effecting a transformation or reduction of a particular article to a different state or thing, and/or an additional element applies or uses 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. See 84 Fed. Reg. 55. In particular, there is a lack of improvement to a computer or technical field in customer sentiment analysis.
The claims do not include additional elements that are sufficient to amount to significantly more than the judicial exception because the additional elements when considered both individually and as an ordered combination do not amount to significantly more than the abstract idea. As discussed above with respect to integration of the abstract idea into a practical application, the additional elements of “computing system”, “memory”, “processor”, “database”, and “computer readable medium” is insufficient to amount to significantly more. (See MPEP 2106.05(f) – Mere Instructions to Apply an Exception – “Thus, for example, claims that amount to nothing more than an instruction to apply the abstract idea using a generic computer do not render an abstract idea eligible.” Alice Corp., 134 S. Ct. at 235). Mere instructions to apply an exception using a generic computer component cannot provide an inventive concept.
The claim fails to recite any improvements to another technology or technical field, improvements to the functioning of the computer itself, use of a particular machine, effecting a transformation or reduction of a particular article to a different state or thing, adding unconventional steps that confine the claim to a particular useful application, and/or meaningful limitations beyond generally linking the use of an abstract idea to a particular environment. See 84 Fed. Reg. 55. Viewed individually or as a whole, these additional claim element(s) do not provide meaningful limitation(s) to transform the abstract idea into a patent eligible application of the abstract idea such that the claim(s) amounts to significantly more than the abstract idea itself. With regards to receiving data and step 2B, it is M2106.05(d)- Receiving or transmitting data over a network, e.g., using the Internet to gather data, Symantec, 838 F.3d at 1321, 120 USPQ2d at 1362 (utilizing an intermediary computer to forward information) and Storing and retrieving information in memory, Versata Dev. Group, Inc. v. SAP Am., Inc., 793 F.3d 1306, 1334, 115 USPQ2d 1681, 1701 (Fed. Cir. 2015).
Examiner concludes that the additional elements in combination fail to amount to significantly more than the abstract idea based on findings that each element merely performs the same function(s) in combination as each element performs separately. The claim is not patent eligible. Thus, taken alone, the additional elements do not amount to significantly more than the above-identified judicial exception (the abstract idea). Looking at the limitations as an ordered combination adds nothing that is not already present when looking at the elements taken individually.
Dependent Claims 2-11, and 13-19 recite receive the communication data of the one or more corresponding customer communications, wherein the communication data comprises character strings; wherein the emotional profile includes one or more other customers having congruent emotion factor values with the customer; apply the one or more sets of emotion factor values for the communication data to the emotion propensity model as input; and indicate, as output from the emotion propensity model, the emotional profile for the customer; create the set of training data that includes the plurality of customer communications, wherein each customer communication comprises the set of emotion factor values and the label identifying the emotional profile for the customer associated with the customer communication; create the set of training data that includes the plurality of customer communications representative of the particular type of customer engagement, wherein each customer communication comprises the set of emotion factor values and a label indicating a preference for the particular type of customer engagement; receive at least a first set of emotion factor values for a first communication associated with the customer having a first time stamp, and a second set of emotion factor values for a second communication associated with the customer having a second time stamp, and wherein to classify the customer, the one or more processors are configured to: compute a composite score for each of the first set of emotion factor values and the second set of emotion factor values; and classify the customer into the emotional profile based on one or more composite scores for the communication data associated with the customer over time; receive at least a first set of emotion factor values for a first communication associated with the customer having a first time stamp, and a second set of emotion factor values for a second communication associated with the customer having a second time stamp, and wherein to classify the customer, the one or more processors are configured to: extract an aggression value from each of the first set of emotion factor values and the second set of emotion factor values; and classify the customer into the emotional profile based on one or more aggression values for the communication data associated with the customer over time; wherein the particular type of customer engagement comprises one of new product solicitation communications or an emotional style of customer interaction communications; and further narrowing the abstract idea. These recited limitations in the dependent claims do not amount to significantly more than the above-identified judicial exceptions in Claims 1, 12 and 20. Regarding Claims, 2, 5-10, 13-19, and the additional elements of “processor” it is M2106.05(d)- Receiving or transmitting data over a network, e.g., using the Internet to gather data, Symantec, 838 F.3d at 1321, 120 USPQ2d at 1362 (utilizing an intermediary computer to forward information). Regarding claims 2-3, 6-8, 13, and 15-17 and the additional element of machine learning model - the specification discloses the machine learning at a high-level of generality, providing examples of different techniques that may be applied. The general use of a machine learning technique does not provide a meaningful limitation to transform the abstract idea into a practical application. Therefore, currently, the machine learning is solely used a tool to perform the instructions of the abstract idea.
Reasons Claims are Patentably Distinguishable from the Prior Art
Examiner analyzed Claims 1-20 in view of the prior art on record and finds not all claim limitations are explicitly taught nor would one of ordinary skill in the art find it obvious to combine these references with a reasonable expectation of success as discussed below.
Examiner analyzed Claims 1-20 in view of the prior art on record and finds not all claim limitations are explicitly taught nor would one of ordinary skill in the art find it obvious to combine these references with a reasonable expectation of success as discussed below.
In regards to Claim 1 (similarly Claim 12 and Claim 20), the prior art does not teach or fairly suggest:
“…wherein each emotion factor value of a given set of emotion factor values indicates a measure of a different emotion in a corresponding customer communication, and wherein the given set of emotion factor values comprises a determination value for the corresponding customer communication, an inquisitiveness value for the corresponding customer communication, a valence value for the corresponding customer communication, and an aggression value for the corresponding customer communication; classify, using an emotion propensity model running on the one or more processors, the customer into an emotional profile according to a typical emotional response of the customer based on the one or more sets of emotion factor values for the communication data of the one or more corresponding customer communications associated with the customer over time, wherein the emotion propensity model comprises a machine learning model trained on a set of training data that includes a plurality of customer communications and, for each customer communication in the plurality of customer communications, a set of emotion factor values and a label identifying an emotional profile for a customer associated with the customer communication; determine a probability that the customer will respond positively to a particular type of customer engagement based on the emotional profile for the customer; and re-train the emotion propensity model based on an updated set of training data, wherein the updated set of training data includes the one or more corresponding customer communications, the one or more sets of emotion factor values, and labels identifying the emotional profile for the customer associated with the one or more corresponding customer communications.”.
Examiner finds that McCord, US Publication No. 20190050875A1 teaches a system and method for customer interaction and experience enhancement which automatically gathers direct and indirect customer communications about products and services, converts them to text where necessary, and analyzes the communications for sentiment and emotional content, and scores and displays the information in a manner conducive to making business decisions based on the customer sentiment and emotion, such as making changes to products or services, troubleshooting customer service interactions, and better marketing.. [Abstract]. In particular, McCord discloses the analysis of the emotional content of text-based information can be broadly separated into two forms: sentiment analysis and emotion analysis..(Par. 28-29; Par. 52-53).
Mostafa, Modelling and Analysing Behaviours and Emotions via Complex User Interactions, arXiv:1902.07683, February 20, 2019, teaches how to make sense of the ever-increasing volume of big social data so that we can better understand and improve the user experience in increasingly complex, data-driven digital systems. This link with usability and the user experience of data-driven system bridges into the wider field of human-computer interaction (HCI),... The fact that the data largely posted on social networks tends to be textual, provides a further link to linguistics, psychology and psycholinguistics to better understand the relationship between human behaviours offline and online.[Abstract]) .
Mathangi et al., US Publication No. 20200013071A1, teaches receiving an input corresponding to at least one of a business objective and a customer interaction channel. The apparatus selects a customer classification framework from among a plurality of customer classification frameworks based on the input. The customer classification framework is associated with a plurality of persona types, where each persona type from among the plurality of persona types is associated with a set of behavioral traits. The apparatus predicts a persona type for a customer during an interaction on the customer interaction channel, where the persona type is predicted from among the plurality of persona types. The apparatus predicts a propensity of the customer to perform at least one action based on the persona type. The apparatus facilitates a provisioning of personalized interaction experience to the customer based on the predicted propensity of the customer to perform the at least one action.[Par. 6]) .
Although McCord, Mostafa, and Mathangi teach machine learning elements of the claim, none of the cited prior art, singularly or in combination, teach or fairly suggest, the combination of, training and retraining of machine learning model and emotion propensity model.. Therefore, for at least these reasons, Claim 1 (similarly Claim 20) is eligible over the prior art.
The dependent claims 2-11 and 13-19 are eligible under 35 U.S.C. 102 and 35 U.S.C. 103 because they depend on claim 1 and claim 12 that is determined to be eligible.
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure: US Patent No. . 11900407B1 to Kaira et al.- Systems and methods associated with providing content personalization are disclosed. In one embodiment, an exemplary method may comprise receiving first data including content preferences associated with an audience, receiving second data including an initial digital message being proposed for transmission to the audience, generating a recommendation data set based at least in part on the first data and the second data, wherein the recommendation data set identifies at least one recommended content type and at least one recommended message type, determining via a natural language generation machine learning model suggested content for the audience, and providing the suggested content for dissemination to the audience. (Abstract)”
Any inquiry concerning this communication or earlier communications from the examiner should be directed to Chesiree Walton, whose telephone number is (571) 272-5219. The examiner can normally be reached from Monday to Friday between 8 AM and 5 PM. If any attempt to reach the examiner by telephone is unsuccessful, the examiner’s supervisor, Patricia Munson, can be reached at (571) 270-5396. The fax telephone numbers for this group are either (571) 273-8300 or (703) 872-9326 (for official communications including After Final communications labeled “Box AF”).
Another resource that is available to applicants is the Patent Application Information Retrieval (PAIR). Information regarding the status of an application can be obtained from the (PAIR) system. Status information for published applications may be obtained from either Private PAIR. Status information for unpublished applications is available through Private PAIR only. For more information about the PAIR system, see http://pair-direct.uspto.gov. Should you have questions on access to the Private PAIR system, please feel free to contact the Electronic Business Center (EBC) at 866-217-9197 (toll-free).
Applicants are invited to contact the Office to schedule an in-person interview to discuss and resolve the issues set forth in this Office Action. Although an interview is not required, the Office believes that an interview can be of use to resolve any issues related to a patent application in an efficient and prompt manner.
Sincerely,
/CHESIREE A WALTON/ Examiner, Art Unit 3624