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 .
DETAILED ACTION
Status of the Application
The following is a non-Final Office Action.
In response to Examiner's communication of 1/12/2026, Applicant responded on 5/12/2026. Amended claim 1, 3, 8, 10-11, 13-14, and 18. Canceled claim 9.
Claims 1-8 and 10-20 are pending in this application have been examined.
Continued Examination Under 37 CFR 1.114
A request for continued examination under 37 CFR 1.114, including the fee set forth in 37 CFR 1.17(e), was filed in this application after final rejection. Since this application is eligible for continued examination under 37 CFR 1.114, and the fee set forth in 37 CFR 1.17(e) has been timely paid, the finality of the previous Office action has been withdrawn pursuant to 37 CFR 1.114. Applicant's submission filed on 5/12/2026 has been entered.
Response to Amendment
Applicant's amendments to claims 1, 3, 8, 10-11, 13-14, and 18 are not sufficient to overcome the 35 USC 101 rejections set forth in the previous action.
Applicant's amendments to claims 1, 3, 8, 10-11, 13-14, and 18 are sufficient to overcome the prior art rejections set forth in the previous action.
Response to Arguments – 35 USC § 101
Applicant’s arguments with respect to the rejections have been fully considered, but they are not persuasive.
Applicant submits, “…As discussed in the current application, and as recited in the claims, embodiments generally relate to determining a rapport score for a contact. In particular, the present technology is directed to determining rapport metrics to determine a rapport score while optimizing the rapport as non-subjective feedback on rapport between participants and granular evaluation of contact data…Claim 1 recites additional limitations of "determining a rapport score based on the first normalized rapport metric and the second normalized rapport metric to optimize the rapport score as non-subjective feedback on rapport between participants of the contact and granular evaluations of the contact data during the contact in real-time according to a combination of the level of emotion, a set of the question and the response, and the mirroring," The additional limitations are not abstract idea grouping because it is impractical for the human mind to perform the determining rapport score to be optimized as non-subjective feedback on rapport between rapport between participants of the contact and granular evaluations of the contact data during the contact in real-time according to a combination of the level of emotion, a set of the question and the response, and the mirroring. The additional limitations are not insignificant extra-solution activities because these limitations are integral part of scoring rapport. The additional limitations, in the claim as a whole, improve the technical field of providing a software user interface that provides information analyzing a rapport of one or more participants of a conversation in real-time. The additional limitations positively recites a technical effect in terms of improvement of software and thus "the claims are directed to an improvement to computer functionality versus being directed to an abstract idea." Id. at 1336. (Desjardins, page 8).) Accordingly, the additional limitations integrate the alleged judicial exception into a practical application….” The Examiner respectfully disagrees.
While Applicant’s amendments further prosecution, however unlike Desjardins, Applicant admits, the claims recite and direct to, …determining a specific score and providing the score, namely a rapport score… real-time score generation during a contact… the current application, and as recited in the claims, embodiments generally relate to determining a rapport score for a contact… determining rapport metrics to determine a rapport score while optimizing the rapport as non-subjective feedback on rapport between participants and granular evaluation of contact data…, which is a problem directed to organizing human activity (i.e. determine human agent emotion scores and rapport scores with human customers) and a mental process (i.e. determine human agent emotion scores and rapport scores with human customers) and mathematical concepts (i.e. applying and performing mathematical models to determine human agent emotions scores and rapport scores with human customers, cosine similarity between vectorized pairs), as established in Step 2A Prong 1. This problem does not specifically arise in the realm of computer technology, but rather, this problem existed and was addressed long before the advent of computers. Thus, the claims do not recite a technical improvement to a technical problem or necessarily roots in computing technologies. And since, the recited elements do not identify any specific machine learning models or specific machine learning steps, under the broadest reasonable interpretation, the recited models are being interpretated as mental processes and mathematical models, and humans reading and comparing acoustic metadata to determine human emotions. Thus, pursuant to the broadest reasonable interpretation, as an ordered combination, each of the additional elements are computing elements recited at high level of generality implementing the abstract idea, and thus, are no more than applying the abstract idea with generic computer components. Further, these additional elements generally link the abstract idea to a technical environment, namely the environment of a computer, performing extra solution activities. Therefore, as a whole, the additional elements do not integrate the abstract ideas into a practical application in Step 2A Prong 2.
The limitations are abstract elements that are part of and directed to the recited abstract idea as described above with respect to the first prong of Step 2A, i.e. mental process, organizing human activities and mathematical concepts, applied with generic computing components and generally linked to a technical environment, i.e. computer. Even novel and newly discovered judicial exceptions are still exceptions, despite their novelty. July 2015 Update, p. 3; see SAP America Inc. v. Investpic, LLC, No. 2017-2081, slip op. at 2 (Fed Cir. May 15, 2018).
Simply reciting specific limitations that narrow the abstract idea does not make an abstract idea non-abstract. 79 Fed. Reg. 74631; buySAFE Inc. v. Google, Inc., 765 F.3d 1350, 1355 (2014); see SAP America at p. 12. As discussed in SAP America, no matter how much of an advance the claims recite, when “the advance lies entirely in the realm of abstract ideas, with no plausibly alleged innovation in the non-abstract application realm,” “[a]n advance of that nature is ineligible for patenting.” Id. at p. 3.
As stated in the MPEP, "an improvement in the abstract idea itself ... is not an improvement in technology." MPEP 2106.05(a). Mere automation of a manual process or a business method being applied on a general purpose computer is not sufficient to show an improvement in computers or other technology, and the claim must include more than mere instructions to perform the method on a generic component or machinery to qualify as an improvement to an existing technology. MPEP 2106.05(a). Thus, Applicant’s claims do not recite an improvement in technology or integrate into a practical application, but rather mental processes and mathematical concepts implemented using or applying generic computer components.
Claims can recite a mental process even if they are claimed as being performed on a computer. The Supreme Court recognized this in Benson, determining that a mathematical algorithm for converting binary coded decimal to pure binary within a computer’s shift register was an abstract idea. The Court concluded that the algorithm could be performed purely mentally even though the claimed procedures “can be carried out in existing computers long in use, no new machinery being necessary.” 409 U.S at 67, 175 USPQ at 675. See also Mortgage Grader, 811 F.3d at 1324, 117 USPQ2d at 1699 (concluding that concept of “anonymous loan shopping” recited in a computer system claim is an abstract idea because it could be “performed by humans without a computer”).
Performing a mental process on a generic computer. An example of a case identifying a mental process performed on a generic computer as an abstract idea is Voter Verified, Inc. v. Election Systems & Software, LLC, 887 F.3d 1376, 1385, 126 USPQ2d 1498, 1504 (Fed. Cir. 2018). In this case, the Federal Circuit relied upon the specification in explaining that the claimed steps of voting, verifying the vote, and submitting the vote for tabulation are “human cognitive actions” that humans have performed for hundreds of years. The claims therefore recited an abstract idea, despite the fact that the claimed voting steps were performed on a computer. 887 F.3d at 1385, 126 USPQ2d at 1504. Another example is Versata, in which the patentee claimed a system and method for determining a price of a product offered to a purchasing organization that was implemented using general purpose computer hardware. 793 F.3d at 1312-13, 1331, 115 USPQ2d at 1685, 1699. The Federal Circuit acknowledged that the claims were performed on a generic computer, but still described the claims as “directed to the abstract idea of determining a price, using organizational and product group hierarchies, in the same way that the claims in Alice were directed to the abstract idea of intermediated settlement, and the claims in Bilski were directed to the abstract idea of risk hedging.” 793 F.3d at 1333; 115 USPQ2d at 1700-01.
Performing a mental process in a computer environment. An example of a case identifying a mental process performed in a computer environment as an abstract idea is Symantec Corp., 838 F.3d at 1316-18, 120 USPQ2d at 1360. In this case, the Federal Circuit relied upon the specification when explaining that the claimed electronic post office, which recited limitations describing how the system would receive, screen and distribute email on a computer network, was analogous to how a person decides whether to read or dispose of a particular piece of mail and that “with the exception of generic computer-implemented steps, there is nothing in the claims themselves that foreclose them from being performed by a human, mentally or with pen and paper”. 838 F.3d at 1318, 120 USPQ2d at 1360. Another example is FairWarning IP, LLC v. Iatric Sys., Inc., 839 F.3d 1089, 120 USPQ2d 1293 (Fed. Cir. 2016). The patentee in FairWarning claimed a system and method of detecting fraud and/or misuse in a computer environment, in which information regarding accesses of a patient’s personal health information was analyzed according to one of several rules (i.e., related to accesses in excess of a specific volume, accesses during a pre-determined time interval, or accesses by a specific user) to determine if the activity indicates improper access. 839 F.3d. at 1092, 120 USPQ2d at 1294. The court determined that these claims were directed to a mental process of detecting misuse, and that the claimed rules here were “the same questions (though perhaps phrased with different words) that humans in analogous situations detecting fraud have asked for decades, if not centuries.” 839 F.3d. at 1094-95, 120 USPQ2d at 1296.
Using a computer as a tool to perform a mental process. An example of a case in which a computer was used as a tool to perform a mental process is Mortgage Grader, 811 F.3d. at 1324, 117 USPQ2d at 1699. The patentee in Mortgage Grader claimed a computer-implemented system for enabling borrowers to anonymously shop for loan packages offered by a plurality of lenders, comprising a database that stores loan package data from the lenders, and a computer system providing an interface and a grading module. The interface prompts a borrower to enter personal information, which the grading module uses to calculate the borrower’s credit grading, and allows the borrower to identify and compare loan packages in the database using the credit grading. 811 F.3d. at 1318, 117 USPQ2d at 1695. The Federal Circuit determined that these claims were directed to the concept of “anonymous loan shopping”, which was a concept that could be “performed by humans without a computer.” 811 F.3d. at 1324, 117 USPQ2d at 1699. Another example is Berkheimer v. HP, Inc., 881 F.3d 1360, 125 USPQ2d 1649 (Fed. Cir. 2018), in which the patentee claimed methods for parsing and evaluating data using a computer processing system. The Federal Circuit determined that these claims were directed to mental processes of parsing and comparing data, because the steps were recited at a high level of generality and merely used computers as a tool to perform the processes. 881 F.3d at 1366, 125 USPQ2d at 1652-53. See MPEP 2106.04(a)(2).
Further, the courts have indicated may not be sufficient to show an improvement in computer-functionality:
i. Generating restaurant menus with functionally claimed features, Ameranth, 842 F.3d at 1245, 120 USPQ2d at 1857;
ii. Accelerating a process of analyzing audit log data when the increased speed comes solely from the capabilities of a general-purpose computer, FairWarning IP, LLC v. Iatric Sys., 839 F.3d 1089, 1095, 120 USPQ2d 1293, 1296 (Fed. Cir. 2016);
iii. Mere automation of manual processes, such as using a generic computer to process an application for financing a purchase, Credit Acceptance Corp. v. Westlake Services, 859 F.3d 1044, 1055, 123 USPQ2d 1100, 1108-09 (Fed. Cir. 2017) or speeding up a loan-application process by enabling borrowers to avoid physically going to or calling each lender and filling out a loan application, LendingTree, LLC v. Zillow, Inc., 656 Fed. App'x 991, 996-97 (Fed. Cir. 2016) (non-precedential);
vii. Providing historical usage information to users while they are inputting data, in order to improve the quality and organization of information added to a database, because “an improvement to the information stored by a database is not equivalent to an improvement in the database’s functionality,” BSG Tech LLC v. Buyseasons, Inc., 899 F.3d 1281, 1287-88, 127 USPQ2d 1688, 1693-94 (Fed. Cir. 2018); and
viii. Arranging transactional information on a graphical user interface in a manner that assists traders in processing information more quickly, Trading Technologies v. IBG LLC, 921 F.3d 1084, 1093-94, 2019 USPQ2d 138290 (Fed. Cir. 2019).
And, the courts have indicated may not be sufficient to show an improvement to technology include:
i. A commonplace business method being applied on a general purpose computer, Alice Corp., 573 U.S. at 223, 110 USPQ2d at 1976; Versata Dev. Group, Inc. v. SAP Am., Inc., 793 F.3d 1306, 1334, 115 USPQ2d 1681, 1701 (Fed. Cir. 2015);
iii. Gathering and analyzing information using conventional techniques and displaying the result, TLI Communications, 823 F.3d at 612-13, 118 USPQ2d at 1747-48;
Use of a computer or other machinery in its ordinary capacity for economic or other tasks (e.g., to receive, store, or transmit data) or simply adding a general purpose computer or computer components after the fact to an abstract idea (e.g., a fundamental economic practice or mathematical equation) does not integrate a judicial exception into a practical application or provide significantly more. See Affinity Labs v. DirecTV, 838 F.3d 1253, 1262, 120 USPQ2d 1201, 1207 (Fed. Cir. 2016) (cellular telephone); TLI Communications LLC v. AV Auto, LLC, 823 F.3d 607, 613, 118 USPQ2d 1744, 1748 (Fed. Cir. 2016) (computer server and telephone unit). Similarly, “claiming the improved speed or efficiency inherent with applying the abstract idea on a computer” does not integrate a judicial exception into a practical application or provide an inventive concept. Intellectual Ventures I LLC v. Capital One Bank (USA), 792 F.3d 1363, 1367, 115 USPQ2d 1636, 1639 (Fed. Cir. 2015). In contrast, a claim that purports to improve computer capabilities or to improve an existing technology may integrate a judicial exception into a practical application or provide significantly more. McRO, Inc. v. Bandai Namco Games Am. Inc., 837 F.3d 1299, 1314-15, 120 USPQ2d 1091, 1101-02 (Fed. Cir. 2016); Enfish, LLC v. Microsoft Corp., 822 F.3d 1327, 1335-36, 118 USPQ2d 1684, 1688-89 (Fed. Cir. 2016). See MPEP §§ 2106.04(d)(1) and 2106.05(a) for a discussion of improvements to the functioning of a computer or to another technology or technical field.
TLI Communications provides an example of a claim invoking computers and other machinery merely as a tool to perform an existing process. The court stated that the claims describe steps of recording, administration and archiving of digital images, and found them to be directed to the abstract idea of classifying and storing digital images in an organized manner. 823 F.3d at 612, 118 USPQ2d at 1747. The court then turned to the additional elements of performing these functions using a telephone unit and a server and noted that these elements were being used in their ordinary capacity (i.e., the telephone unit is used to make calls and operate as a digital camera including compressing images and transmitting those images, and the server simply receives data, extracts classification information from the received data, and stores the digital images based on the extracted information). 823 F.3d at 612-13, 118 USPQ2d at 1747-48. In other words, the claims invoked the telephone unit and server merely as tools to execute the abstract idea. Thus, the court found that the additional elements did not add significantly more to the abstract idea because they were simply applying the abstract idea on a telephone network without any recitation of details of how to carry out the abstract idea.
Other examples where the courts have found the additional elements to be mere instructions to apply an exception, because they do no more than merely invoke computers or machinery as a tool to perform an existing process include:
i. A commonplace business method or mathematical algorithm being applied on a general purpose computer, Alice Corp. Pty. Ltd. V. CLS Bank Int’l, 573 U.S. 208, 223, 110 USPQ2d 1976, 1983 (2014); Gottschalk v. Benson, 409 U.S. 63, 64, 175 USPQ 673, 674 (1972); Versata Dev. Group, Inc. v. SAP Am., Inc., 793 F.3d 1306, 1334, 115 USPQ2d 1681, 1701 (Fed. Cir. 2015);
ii. Generating a second menu from a first menu and sending the second menu to another location as performed by generic computer components, Apple, Inc. v. Ameranth, Inc., 842 F.3d 1229, 1243-44, 120 USPQ2d 1844, 1855-57 (Fed. Cir. 2016);
iii. A process for monitoring audit log data that is executed on a general-purpose computer where the increased speed in the process comes solely from the capabilities of the general-purpose computer, FairWarning IP, LLC v. Iatric Sys., 839 F.3d 1089, 1095, 120 USPQ2d 1293, 1296 (Fed. Cir. 2016);
iv. A method of using advertising as an exchange or currency being applied or implemented on the Internet, Ultramercial, Inc. v. Hulu, LLC, 772 F.3d 709, 715, 112 USPQ2d 1750, 1754 (Fed. Cir. 2014);
v. Requiring the use of software to tailor information and provide it to the user on a generic computer, Intellectual Ventures I LLC v. Capital One Bank (USA), 792 F.3d 1363, 1370-71, 115 USPQ2d 1636, 1642 (Fed. Cir. 2015);
Response to Arguments – Prior Art
Applicant’s arguments with respect to the rejections have been fully considered.
The closest prior art are US Patent Publication to US20210021709A1 to Scodary et al., (hereinafter referred to as “Scodary”). in view of US Patent Publication to US20210264909A1 to Reece et al., (hereinafter referred to as “Reece”).
However, the teachings of the references do not teach the specific ordered sequence of limitations of independent claims 1, 14, 18,
Claim 1: A computer-implemented method for determining a rapport score for a contact, the method comprising:
receiving contact data associated with a contact;
determining at least a first rapport metric, a second rapport metric, and a third rapport metric from the contact by applying at least a first rapport model, a second rapport model, and a third rapport model to the contact data, wherein:
the first rapport model is an emotional machine learning model trained to determine an emotional state of a contact based upon the contact data, the emotional model analyzing acoustic property metadata and contact data by comparing the emotional state for multiple utterances in the contact data to determine a level of emotion for the contact as the first rapport metric,
wherein the second rapport model is a machine-learning classifier trained to identify a question posed by a first participant and a response to the question, thereby indicating a measure of a first participant having resolved questions posed by a second participant as the second rapport metric, wherein the first rapport metric and the second rapport metric are different, and
wherein the third rapport model is a mirroring model which processes audio data to determine whether the first participant is mirroring the second participant, wherein the mirroring model determines the mirroring as non-subjective feedback on rapport according to similarity between features of the contact data as described by a cosine similarity between vectorized pairs of statements/responses;
generating a first normalized rapport metric and a second normalized rapport metric;
determining a rapport score based on the first normalized rapport metric and the second normalized rapport metric to optimize the rapport score as non-subjective feedback on rapport between participants of the contact and granular evaluations of the contact data during the contact in real-time according to a combination of the level of emotion, a set of the question and the response, and the mirroring; and
providing the rapport score, in real-time, during the contact as real-time feedback.
Claim 14: A system, comprising: a processor; and a memory storing computer-executable instructions that when executed by the processor cause the system to:
receive contact data associated with a contact;
select a first rapport model, a second rapport model, and a third rapport model from a plurality of rapport models, wherein:
the first rapport model is an emotional machine learning model trained to determine an emotional state of a contact based upon the contact data, the emotional model analyzing acoustic property metadata and contact data by comparing the emotional state for multiple utterances in the contact data to determine a level of emotion for the contact as a first rapport metric,
wherein the second rapport model is a machine-learning classifier trained to identify a question posed by a first participant and a response to the question, thereby indicating a measure of a first participant having resolved questions posed by a second participant as a second rapport metric, the first rapport metric and the second rapport metric are distinct, and
wherein the third rapport model is a mirroring model which processes audio data to determine whether the first participant is mirroring the second participant, wherein the mirroring model determines the mirroring as non- subjective feedback on rapport according to similarity between features of the contact data as described by a cosine similarity between vectorized pairs of statements/responses;
determine a rapport metric from the contact, wherein the report metric is generated by executing the first rapport model and the second rapport model on the contact data, wherein the rapport metric is selected from the first rapport metric generated by the first rapport metric and a second rapport metric generated by the second rapport model;
retrieve a target rapport metric associated with the rapport metric;
compare the rapport metric to the target rapport metric to determine a rapport metric deviation, wherein the rapport metric deviation indicates an amount of deviation of the rapport metric from the target rapport metric;
determine a rapport score based on the rapport metric deviation to optimize the rapport score as non-subjective feedback on rapport between participants of the contact and granular evaluations of the contact data during the contact in real-time according to a combination of the level of emotion, a set of the question and the response, and the mirroring; and
provide the rapport score in real-time during the contact as real-time feedback.
Claim 18: A computer storage medium encoding computer executable instructions that, when executed by at least one processor, perform a method comprising:
receiving contact data associated with a contact; selecting a first rapport model from a plurality of rapport models, wherein the first rapport model is an emotional machine learning model trained to determine an emotional state of a contact based upon the contact data, the emotional model analyzing acoustic property metadata and contact data by comparing the emotional state for multiple utterances in the contact data to determine a level of emotion for the contact as a first rapport metric;
selecting a second rapport model and a second rapport metric from a plurality of rapport models, wherein the second rapport model is a machine-learning classifier trained to identify a question posed by a first participant and a response to the question, thereby indicating a measure of a first participant having resolved questions posed by a second participant as the second rapport metric, wherein the first rapport metric and the second rapport metric are different;
selecting a mirroring model which processes audio data to determine whether a first participant is mirroring a second participant, wherein the mirroring model determines the mirroring as non-subjective feedback on rapport according to similarity between features of the contact data as described by a cosine similarity between vectorized pairs of statements/responses;
determining the first rapport metric from the contact by applying the first rapport model to the contact data;
determining the second rapport metric from the contact by applying the second rapport model to the contact data;
generalizing a first normalized rapport metric and a second normalized rapport metric;
determining a rapport score based on the first normalized rapport metric, the second rapport normalized metric, and the mirroring determination, to optimize the rapport score as non- subjective feedback on rapport between participants of the contact and granular evaluations of the contact data during the contact in real-time according to a combination of the level of emotion, a set of the question and the response, and the mirroring; and
providing the rapport score in real-time during the contact as real-time feedback.
No Non-Patent literature teach the specific ordered sequence of limitations of independent claims 1, 14, 18.
The prior art rejection is hereby withdrawn.
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 1-8 and 10-20 are rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more.
Claim 1 recites, “A ... method for determining a rapport score for a contact, the method comprising:
receiving contact data associated with a contact;
determining at least a first rapport metric, a second rapport metric, and a third rapport metric from the contact by applying at least a first rapport model, a second rapport model, and a third rapport model to the contact data, wherein:
the first rapport model is an emotional … model trained to determine an emotional state of a contact based upon the contact data, the emotional model analyzing acoustic property metadata and contact data by comparing the emotional state for multiple utterances in the contact data to determine a level of emotion for the contact as the first rapport metric,
wherein the second rapport model is a … classifier trained to identify a question posed by a first participant and a response to the question, thereby indicating a measure of a first participant having resolved questions posed by a second participant as the second rapport metric, wherein the first rapport metric and the second rapport metric are different, and
wherein the third rapport model is a mirroring model which processes audio data to determine whether the first participant is mirroring the second participant, wherein the mirroring model determines the mirroring as non-subjective feedback on rapport according to similarity between features of the contact data as described by a cosine similarity between vectorized pairs of statements/responses;
generating a first normalized rapport metric and a second normalized rapport metric;
determining a rapport score based on the first normalized rapport metric and the second normalized rapport metric to optimize the rapport score as non-subjective feedback on rapport between participants of the contact and granular evaluations of the contact data during the contact in real-time according to a combination of the level of emotion, a set of the question and the response, and the mirroring; and
providing the rapport score, in real-time, during the contact as real-time feedback.”
Claim 14 recites, “... to:
receive contact data associated with a contact;
select a first rapport model, a second rapport model, and a third rapport model from a plurality of rapport models, wherein:
the first rapport model is an emotional … model trained to determine an emotional state of a contact based upon the contact data, the emotional model analyzing acoustic property metadata and contact data by comparing the emotional state for multiple utterances in the contact data to determine a level of emotion for the contact as a first rapport metric,
wherein the second rapport model is a … classifier trained to identify a question posed by a first participant and a response to the question, thereby indicating a measure of a first participant having resolved questions posed by a second participant as a second rapport metric, the first rapport metric and the second rapport metric are distinct, and
wherein the third rapport model is a mirroring model which processes audio data to determine whether the first participant is mirroring the second participant, wherein the mirroring model determines the mirroring as non- subjective feedback on rapport according to similarity between features of the contact data as described by a cosine similarity between vectorized pairs of statements/responses;
determine a rapport metric from the contact, wherein the report metric is generated by executing the first rapport model and the second rapport model on the contact data, wherein the rapport metric is selected from the first rapport metric generated by the first rapport metric and a second rapport metric generated by the second rapport model;
retrieve a target rapport metric associated with the rapport metric;
compare the rapport metric to the target rapport metric to determine a rapport metric deviation, wherein the rapport metric deviation indicates an amount of deviation of the rapport metric from the target rapport metric;
determine a rapport score based on the rapport metric deviation to optimize the rapport score as non-subjective feedback on rapport between participants of the contact and granular evaluations of the contact data during the contact in real-time according to a combination of the level of emotion, a set of the question and the response, and the mirroring; and
provide the rapport score in real-time during the contact as real-time feedback.”
Claim 18 recites, “..., perform a method comprising:
receiving contact data associated with a contact;
selecting a first rapport model from a plurality of rapport models, wherein the first rapport model is an emotional machine learning model trained to determine an emotional state of a contact based upon the contact data, the emotional model analyzing acoustic property metadata and contact data by comparing the emotional state for multiple utterances in the contact data to determine a level of emotion for the contact as a first rapport metric;
selecting a second rapport model and a second rapport metric from a plurality of rapport models, wherein the second rapport model is a … classifier trained to identify a question posed by a first participant and a response to the question, thereby indicating a measure of a first participant having resolved questions posed by a second participant as the second rapport metric, wherein the first rapport metric and the second rapport metric are different;
selecting a mirroring model which processes audio data to determine whether a first participant is mirroring a second participant, wherein the mirroring model determines the mirroring as non-subjective feedback on rapport according to similarity between features of the contact data as described by a cosine similarity between vectorized pairs of statements/responses;
determining the first rapport metric from the contact by applying the first rapport model to the contact data;
determining the second rapport metric from the contact by applying the second rapport model to the contact data;
generalizing a first normalized rapport metric and a second normalized rapport metric;
determining a rapport score based on the first normalized rapport metric, the second rapport normalized metric, and the mirroring determination, to optimize the rapport score as non- subjective feedback on rapport between participants of the contact and granular evaluations of the contact data during the contact in real-time according to a combination of the level of emotion, a set of the question and the response, and the mirroring; and
providing the rapport score in real-time during the contact as real-time feedback.”
Analyzing under Step 2A, Prong 1:
The limitations regarding, …receiving contact data associated with a contact; determining at least a first rapport metric, a second rapport metric, and a third rapport metric from the contact by applying at least a first rapport model, a second rapport model, and a third rapport model to the contact data, wherein: the first rapport model is an emotional … model trained to determine an emotional state of a contact based upon the contact data, the emotional model analyzing acoustic property metadata and contact data by comparing the emotional state for multiple utterances in the contact data to determine a level of emotion for the contact as the first rapport metric, wherein the second rapport model is a … classifier trained to identify a question posed by a first participant and a response to the question, thereby indicating a measure of a first participant having resolved questions posed by a second participant as the second rapport metric, wherein the first rapport metric and the second rapport metric are different, and wherein the third rapport model is a mirroring model which processes audio data to determine whether the first participant is mirroring the second participant, wherein the mirroring model determines the mirroring as non-subjective feedback on rapport according to similarity between features of the contact data as described by a cosine similarity between vectorized pairs of statements/responses; generating a first normalized rapport metric and a second normalized rapport metric; determining a rapport score based on the first normalized rapport metric and the second normalized rapport metric to optimize the rapport score as non-subjective feedback on rapport between participants of the contact and granular evaluations of the contact data during the contact in real-time according to a combination of the level of emotion, a set of the question and the response, and the mirroring; and providing the rapport score, in real-time, during the contact as real-time feedback…receive contact data associated with a contact; select a first rapport model, a second rapport model, and a third rapport model from a plurality of rapport models, wherein: the first rapport model is an emotional … model trained to determine an emotional state of a contact based upon the contact data, the emotional model analyzing acoustic property metadata and contact data by comparing the emotional state for multiple utterances in the contact data to determine a level of emotion for the contact as a first rapport metric, wherein the second rapport model is a … classifier trained to identify a question posed by a first participant and a response to the question, thereby indicating a measure of a first participant having resolved questions posed by a second participant as a second rapport metric, the first rapport metric and the second rapport metric are distinct, and wherein the third rapport model is a mirroring model which processes audio data to determine whether the first participant is mirroring the second participant, wherein the mirroring model determines the mirroring as non- subjective feedback on rapport according to similarity between features of the contact data as described by a cosine similarity between vectorized pairs of statements/responses; determine a rapport metric from the contact, wherein the report metric is generated by executing the first rapport model and the second rapport model on the contact data, wherein the rapport metric is selected from the first rapport metric generated by the first rapport metric and a second rapport metric generated by the second rapport model; retrieve a target rapport metric associated with the rapport metric; compare the rapport metric to the target rapport metric to determine a rapport metric deviation, wherein the rapport metric deviation indicates an amount of deviation of the rapport metric from the target rapport metric; determine a rapport score based on the rapport metric deviation to optimize the rapport score as non-subjective feedback on rapport between participants of the contact and granular evaluations of the contact data during the contact in real-time according to a combination of the level of emotion, a set of the question and the response, and the mirroring; and provide the rapport score in real-time during the contact as real-time feedback… receiving contact data associated with a contact; selecting a first rapport model from a plurality of rapport models, wherein the first rapport model is an emotional machine learning model trained to determine an emotional state of a contact based upon the contact data, the emotional model analyzing acoustic property metadata and contact data by comparing the emotional state for multiple utterances in the contact data to determine a level of emotion for the contact as a first rapport metric; selecting a second rapport model and a second rapport metric from a plurality of rapport models, wherein the second rapport model is a … classifier trained to identify a question posed by a first participant and a response to the question, thereby indicating a measure of a first participant having resolved questions posed by a second participant as the second rapport metric, wherein the first rapport metric and the second rapport metric are different; selecting a mirroring model which processes audio data to determine whether a first participant is mirroring a second participant, wherein the mirroring model determines the mirroring as non-subjective feedback on rapport according to similarity between features of the contact data as described by a cosine similarity between vectorized pairs of statements/responses; determining the first rapport metric from the contact by applying the first rapport model to the contact data; determining the second rapport metric from the contact by applying the second rapport model to the contact data; generalizing a first normalized rapport metric and a second normalized rapport metric; determining a rapport score based on the first normalized rapport metric, the second rapport normalized metric, and the mirroring determination, to optimize the rapport score as non- subjective feedback on rapport between participants of the contact and granular evaluations of the contact data during the contact in real-time according to a combination of the level of emotion, a set of the question and the response, and the mirroring; and providing the rapport score in real-time during the contact as real-time feedback…, under the broadest reasonable interpretation, can include a human using their mind and using pen and paper to perform the identified limitations. Therefore, the claims recite a mental process.
Further, the limitations regarding, …receiving contact data associated with a contact; determining at least a first rapport metric, a second rapport metric, and a third rapport metric from the contact by applying at least a first rapport model, a second rapport model, and a third rapport model to the contact data, wherein: the first rapport model is an emotional … model trained to determine an emotional state of a contact based upon the contact data, the emotional model analyzing acoustic property metadata and contact data by comparing the emotional state for multiple utterances in the contact data to determine a level of emotion for the contact as the first rapport metric, wherein the second rapport model is a … classifier trained to identify a question posed by a first participant and a response to the question, thereby indicating a measure of a first participant having resolved questions posed by a second participant as the second rapport metric, wherein the first rapport metric and the second rapport metric are different, and wherein the third rapport model is a mirroring model which processes audio data to determine whether the first participant is mirroring the second participant, wherein the mirroring model determines the mirroring as non-subjective feedback on rapport according to similarity between features of the contact data as described by a cosine similarity between vectorized pairs of statements/responses; generating a first normalized rapport metric and a second normalized rapport metric; determining a rapport score based on the first normalized rapport metric and the second normalized rapport metric to optimize the rapport score as non-subjective feedback on rapport between participants of the contact and granular evaluations of the contact data during the contact in real-time according to a combination of the level of emotion, a set of the question and the response, and the mirroring; and providing the rapport score, in real-time, during the contact as real-time feedback…receive contact data associated with a contact; select a first rapport model, a second rapport model, and a third rapport model from a plurality of rapport models, wherein: the first rapport model is an emotional … model trained to determine an emotional state of a contact based upon the contact data, the emotional model analyzing acoustic property metadata and contact data by comparing the emotional state for multiple utterances in the contact data to determine a level of emotion for the contact as a first rapport metric, wherein the second rapport model is a … classifier trained to identify a question posed by a first participant and a response to the question, thereby indicating a measure of a first participant having resolved questions posed by a second participant as a second rapport metric, the first rapport metric and the second rapport metric are distinct, and wherein the third rapport model is a mirroring model which processes audio data to determine whether the first participant is mirroring the second participant, wherein the mirroring model determines the mirroring as non- subjective feedback on rapport according to similarity between features of the contact data as described by a cosine similarity between vectorized pairs of statements/responses; determine a rapport metric from the contact, wherein the report metric is generated by executing the first rapport model and the second rapport model on the contact data, wherein the rapport metric is selected from the first rapport metric generated by the first rapport metric and a second rapport metric generated by the second rapport model; retrieve a target rapport metric associated with the rapport metric; compare the rapport metric to the target rapport metric to determine a rapport metric deviation, wherein the rapport metric deviation indicates an amount of deviation of the rapport metric from the target rapport metric; determine a rapport score based on the rapport metric deviation to optimize the rapport score as non-subjective feedback on rapport between participants of the contact and granular evaluations of the contact data during the contact in real-time according to a combination of the level of emotion, a set of the question and the response, and the mirroring; and provide the rapport score in real-time during the contact as real-time feedback… receiving contact data associated with a contact; selecting a first rapport model from a plurality of rapport models, wherein the first rapport model is an emotional machine learning model trained to determine an emotional state of a contact based upon the contact data, the emotional model analyzing acoustic property metadata and contact data by comparing the emotional state for multiple utterances in the contact data to determine a level of emotion for the contact as a first rapport metric; selecting a second rapport model and a second rapport metric from a plurality of rapport models, wherein the second rapport model is a … classifier trained to identify a question posed by a first participant and a response to the question, thereby indicating a measure of a first participant having resolved questions posed by a second participant as the second rapport metric, wherein the first rapport metric and the second rapport metric are different; selecting a mirroring model which processes audio data to determine whether a first participant is mirroring a second participant, wherein the mirroring model determines the mirroring as non-subjective feedback on rapport according to similarity between features of the contact data as described by a cosine similarity between vectorized pairs of statements/responses; determining the first rapport metric from the contact by applying the first rapport model to the contact data; determining the second rapport metric from the contact by applying the second rapport model to the contact data; generalizing a first normalized rapport metric and a second normalized rapport metric; determining a rapport score based on the first normalized rapport metric, the second rapport normalized metric, and the mirroring determination, to optimize the rapport score as non- subjective feedback on rapport between participants of the contact and granular evaluations of the contact data during the contact in real-time according to a combination of the level of emotion, a set of the question and the response, and the mirroring; and providing the rapport score in real-time during the contact as real-time feedback…., under the broadest reasonable interpretation, is managing human agents’ emotions and rapport with human customer contacts, which is managing human behaviors and relationships, thus, the claims recite organizing human activities.
Additionally, the limitations regarding, … receiving contact data associated with a contact; determining at least a first rapport metric, a second rapport metric, and a third rapport metric from the contact by applying at least a first rapport model, a second rapport model, and a third rapport model to the contact data, wherein: the first rapport model is an emotional … model trained to determine an emotional state of a contact based upon the contact data, the emotional model analyzing acoustic property metadata and contact data by comparing the emotional state for multiple utterances in the contact data to determine a level of emotion for the contact as the first rapport metric, wherein the second rapport model is a … classifier trained to identify a question posed by a first participant and a response to the question, thereby indicating a measure of a first participant having resolved questions posed by a second participant as the second rapport metric, wherein the first rapport metric and the second rapport metric are different, and wherein the third rapport model is a mirroring model which processes audio data to determine whether the first participant is mirroring the second participant, wherein the mirroring model determines the mirroring as non-subjective feedback on rapport according to similarity between features of the contact data as described by a cosine similarity between vectorized pairs of statements/responses; generating a first normalized rapport metric and a second normalized rapport metric; determining a rapport score based on the first normalized rapport metric and the second normalized rapport metric to optimize the rapport score as non-subjective feedback on rapport between participants of the contact and granular evaluations of the contact data during the contact in real-time according to a combination of the level of emotion, a set of the question and the response, and the mirroring; and providing the rapport score, in real-time, during the contact as real-time feedback…receive contact data associated with a contact; select a first rapport model, a second rapport model, and a third rapport model from a plurality of rapport models, wherein: the first rapport model is an emotional … model trained to determine an emotional state of a contact based upon the contact data, the emotional model analyzing acoustic property metadata and contact data by comparing the emotional state for multiple utterances in the contact data to determine a level of emotion for the contact as a first rapport metric, wherein the second rapport model is a … classifier trained to identify a question posed by a first participant and a response to the question, thereby indicating a measure of a first participant having resolved questions posed by a second participant as a second rapport metric, the first rapport metric and the second rapport metric are distinct, and wherein the third rapport model is a mirroring model which processes audio data to determine whether the first participant is mirroring the second participant, wherein the mirroring model determines the mirroring as non- subjective feedback on rapport according to similarity between features of the contact data as described by a cosine similarity between vectorized pairs of statements/responses; determine a rapport metric from the contact, wherein the report metric is generated by executing the first rapport model and the second rapport model on the contact data, wherein the rapport metric is selected from the first rapport metric generated by the first rapport metric and a second rapport metric generated by the second rapport model; retrieve a target rapport metric associated with the rapport metric; compare the rapport metric to the target rapport metric to determine a rapport metric deviation, wherein the rapport metric deviation indicates an amount of deviation of the rapport metric from the target rapport metric; determine a rapport score based on the rapport metric deviation to optimize the rapport score as non-subjective feedback on rapport between participants of the contact and granular evaluations of the contact data during the contact in real-time according to a combination of the level of emotion, a set of the question and the response, and the mirroring; and provide the rapport score in real-time during the contact as real-time feedback… receiving contact data associated with a contact; selecting a first rapport model from a plurality of rapport models, wherein the first rapport model is an emotional machine learning model trained to determine an emotional state of a contact based upon the contact data, the emotional model analyzing acoustic property metadata and contact data by comparing the emotional state for multiple utterances in the contact data to determine a level of emotion for the contact as a first rapport metric; selecting a second rapport model and a second rapport metric from a plurality of rapport models, wherein the second rapport model is a … classifier trained to identify a question posed by a first participant and a response to the question, thereby indicating a measure of a first participant having resolved questions posed by a second participant as the second rapport metric, wherein the first rapport metric and the second rapport metric are different; selecting a mirroring model which processes audio data to determine whether a first participant is mirroring a second participant, wherein the mirroring model determines the mirroring as non-subjective feedback on rapport according to similarity between features of the contact data as described by a cosine similarity between vectorized pairs of statements/responses; determining the first rapport metric from the contact by applying the first rapport model to the contact data; determining the second rapport metric from the contact by applying the second rapport model to the contact data; generalizing a first normalized rapport metric and a second normalized rapport metric; determining a rapport score based on the first normalized rapport metric, the second rapport normalized metric, and the mirroring determination, to optimize the rapport score as non- subjective feedback on rapport between participants of the contact and granular evaluations of the contact data during the contact in real-time according to a combination of the level of emotion, a set of the question and the response, and the mirroring; and providing the rapport score in real-time during the contact as real-time feedback…..., recite mathematical concepts.
Accordingly, the claims recite a mental process, organizing human activities, mathematical concepts, and thus, the claims are directed to an abstract idea under the first prong of Step 2A.
Analyzing under Step 2A, Prong 2:
This judicial exception is not integrated into a practical application under the second prong of Step 2A.
In particular, the claims recite the additional elements beyond the recited abstract idea identified under Step 2A, Prong 1, such as:
Claim 1, 14, 18: computer-implemented, machine learning, machine-learning classifier, A system, comprising: a processor; and a memory storing computer-executable instructions that when executed by the processor cause the system, A computer storage medium encoding computer executable instructions that, when executed by at least one processor
, and pursuant to the broadest reasonable interpretation, as an ordered combination, each of the additional elements are computing elements recited at high level of generality implementing the abstract idea, and thus, are no more than applying the abstract idea with generic computer components. Further, these additional elements generally link the abstract idea to a technical environment, namely the environment of a computer.
Additionally, with respect to, “receiving...”, “providing...”, these elements do not add a meaningful limitations to integrate the abstract idea into a practical application because they are extra-solution activity, pre and post solution activity - i.e. data gathering – “receiving...”, , data output – “providing...”
Analyzing under Step 2B:
The claim does not include additional elements that are sufficient to amount to significantly more than the judicial exception under Step 2B.
As noted above, the aforementioned additional elements beyond the recited abstract idea are not sufficient to amount to significantly more than the recited abstract idea because, as an order combination, the additional elements are no more than mere instructions to implement the idea using generic computer components (i.e. apply it).
Additionally, as an order combination, the additional elements append the recited abstract idea to well-understood, routine, and conventional activities in the field as individually evinced by the applicant’s own disclosure, as required by the Berkheimer Memo, in at least:
[0022] Figure 1 illustrates an overview of an exemplary system 100 for determining a rapport score from rapport metrics based on contact data. The system 100 may include a client-computing device 102, a computer terminal 104, a virtual assistant server 106, and a rapport engine 110 connected via a network 140. In aspects, the client-computing device 102 may include a smartphone and/or a phone device where a user may participate in a contact or join a conversation with another speaker. The computer terminal 104 may include an operator station where an operator of a contact center may receive incoming contacts from customers (e.g., a user using the client-computing device 102). In alternate aspects, the virtual assistant server 106 may process a virtual assistant for the user using the client-computing device 102 over the network 140. In said scenarios, the user using the client-computing device 102 may join a conversation with a virtual assistant. The network 140 may be a computer communication network. Additionally, or alternatively, the network 140 may include a public or private telecommunication network exchange to interconnect with ordinary phones (e.g., the phone devices).
[0129] Figure 10 illustrates a simplified block diagram of a device with which aspects of the present disclosure may be practiced in accordance with aspects of the present disclosure. The device may be a mobile computing device, for example. One or more of the present embodiments may be implemented in an operating environment 1000. This is only one example of a suitable operating environment and is not intended to suggest any limitation as to the scope of use or functionality. Other well-known computing systems, environments, and/or configurations that may be suitable for use include, but are not limited to, personal computers, server computers, hand- held or laptop devices, multiprocessor systems, microprocessor-based systems, programmable consumer electronics such as smartphones, network PCs, minicomputers, mainframe computers, distributed computing environments that include any of the above systems or devices, and the like.
[0130] In its most basic configuration, the operating environment 1000 typically includes at least one processing unit 1002 and memory 1004. Depending on the exact configuration and type of computing device, memory 1004 (instructions to perform a cellular-communication-assisted PPV as described herein) may be volatile (such as RAM), non-volatile (such as ROM, flash memory, etc.), or some combination of the two. This most basic configuration is illustrated in Figure 10 by dashed line 1006. Further, the operating environment 1000 may also include storage devices (removable, 1008, and/or non-removable, 1010) including, but not limited to, magnetic or optical disks or tape. Similarly, the operating environment 1000 may also have input device(s) 1014 such as remote controller, keyboard, mouse, pen, voice input, on-board sensors, etc. and/or output device(s) 1012 such as a display, speakers, printer, motors, etc. Also included in the environment may be one or more communication connections 1016, such as LAN, WAN, a near- field communications network, a cellular broadband network, point to point, etc.
[0131] Operating environment 1000 typically includes at least some form of computer readable media. Computer readable media can be any available media that can be accessed by processing unit 1002 or other devices comprising the operating environment. By way of example, and not limitation, computer readable media may comprise computer storage media and communication media. Computer storage media includes volatile and nonvolatile, removable and non-removable media implemented in any method or technology for storage of information such as computer readable instructions, data structures, program modules or other data. Computer storage media includes, RAM, ROM, EEPROM, flash memory or other memory technology, CD- ROM, digital versatile disks (DVD) or other optical storage, magnetic cassettes, magnetic tape, magnetic disk storage or other magnetic storage devices, or any other tangible, non-transitory medium which can be used to store the desired information. Computer storage media does not include communication media. Computer storage media does not include a carrier wave or other propagated or modulated data signal.
[0132] Communication media embodies computer readable instructions, data structures, program modules, or other data in a modulated data signal such as a carrier wave or other transport mechanism and includes any information delivery media. The term "modulated data signal" means a signal that has one or more of its characteristics set or changed in such a manner as to encode information in the signal. By way of example, and not limitation, communication media includes wired media such as a wired network or direct-wired connection, and wireless media such as acoustic, RF, infrared and other wireless media.
[0133] The operating environment 1000 may be a single computer operating in a networked environment using logical connections to one or more remote computers. The remote computer may be a personal computer, a server, a router, a network PC, a peer device or other common network node, and typically includes many or all of the elements described above as well as others not so mentioned. The logical connections may include any method supported by available communications media. Such networking environments are commonplace in offices, enterprise- wide computer networks, intranets and the Internet.
[0134] The description and illustration of one or more aspects provided in this application are not intended to limit or restrict the scope of the disclosure as claimed in any way. The claimed disclosure should not be construed as being limited to any aspect, for example, or detail provided in this application. Regardless of whether shown and described in combination or separately, the various features (both structural and methodological) are intended to be selectively included or omitted to produce an embodiment with a particular set of features. Having been provided with the description and illustration of the present application, one skilled in the art may envision variations, modifications, and alternate aspects falling within the spirit of the broader aspects of the general inventive concept embodied in this application that do not depart from the broader scope of the claimed disclosure.
[0135] Any of the one or more above aspects in combination with any other of the one or more aspect. Any of the one or more aspects as described herein.
Furthermore, as an ordered combination, these elements amount to generic computer components receiving or transmitting data over a network, performing repetitive calculations, electronic record keeping, and storing and retrieving information in memory, which, as held by the courts, are well-understood, routine, and conventional. See MPEP 2106.05(d).
Moreover, the remaining elements of dependent claims do not transform the recited abstract idea into a patent eligible invention because these remaining elements merely recite further abstract limitations that provide nothing more than simply a narrowing of the abstract idea recited in the independent claims.
Looking at these limitations as an ordered combination adds nothing additional that is sufficient to amount to significantly more than the recited abstract idea because they simply provide instructions to use a generic arrangement of generic computer components to “apply” the recited abstract idea, perform insignificant extra-solution activity, and generally link the abstract idea to a technical environment. Thus, the elements of the claims, considered both individually and as an ordered combination, are not sufficient to ensure that the claims as a whole amount to significantly more than the abstract idea itself. Since there are no limitations in these claims that transform the exception into a patent eligible application such that these claims amount to significantly more than the exception itself, claims 1-8 and 10-20 are rejected under 35 U.S.C. 101 as being directed to non-statutory subject matter.
Conclusion
Applicant's amendment necessitated the new ground(s) of rejection presented in this Office action.
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/PO HAN LEE/Primary Examiner, Art Unit 3623
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