SYSTEM AND METHOD FOR REDUCING TIME TAKEN FOR EVALUATION OF AN INTERACTION THAT HAS BEEN RECORDED BY A RECORDING-PLAYER WEB-APPLICATION BY USING GENERATIVE AI WITH LARGE LANGUAGE MODELS

1 . A computerized-method for reducing time taken for evaluation of an interaction by annotating a media-file of the interaction that has been recorded by a recording-player web-application based on an evaluation-measurement, said computerized-method comprising:

(i) receiving a request from a user to playback the media-file of the interaction by operating the media-playback service of the recording-player web-application;

(ii) configuring the media-playback service to:

a. operate an interaction-insights module to generate one or more point-in-time annotations of the media-file, based on one or more parameters of the evaluation-measurement; and

b. send the one or more point-in-time annotations and a location of the media-file to the recording-player web-application; and

(iii) configuring the recording-player web-application to playback the media-file and upon a user-selection to present each point-in-time annotation of the one or more point-in-time annotations, via a User Interface (UI) that is associated to the recording-player web-application, on a timeline-bar as an annotation-marker, wherein each point-in-time annotation comprising a playhead position in the media-file and a text-annotation related to a parameter of the one or more parameters of the evaluation-measurement.

2 . The computerized-method of claim 1 , wherein the computerized-method is checking and annotating the media-file of the interaction based on one or more evaluation-measurements.

3 . The computerized-method of claim 1 , wherein the evaluation-measurement is one of: (i) customer demography and identification; (ii) agent behavior analysis; (iii) interaction analytics; and (iv) interaction opening and closing,

wherein the one or more parameters of the customer demography and identification is at least one of: a. customer identity verification; b. identify information compromised; and c. customer demographic check,

wherein the one or more parameters of the agent behavior analysis is at least one of: a. empathy; b. politeness,

wherein the one or more parameters of the interaction analytics is at least one of: a. no delays; b. silence; c. long holds; and d. unnecessary holds, and

wherein the one or more parameters of the interaction opening and closing is at least one of: a. greetings at the opening of the interaction; and b. greetings at the closing of the interaction.

4 . The computerized-method of claim 1 , wherein the user-selection is one of: first user-selection of the annotation-marker on the timeline-bar, to display the text-annotation related to the evaluation measurement on the timeline-bar, and second user-selection of all-annotation-markers, to display each text-annotation related to the evaluation measurement of the one or more point-in-time annotations on the timeline-bar.

5 . The computerized-method of claim 4 , wherein the first user-selection and the second user-selection are operated by at least one of: (i) mouse click; (ii) keystroke; and (iii) keystroke combination.

6 . The computerized-method of claim 4 , wherein each generated point-in-time annotation of the one or more point-in-time annotations of the media-file is stored as an attribute of the interaction in a database.

7 . The computerized-method of claim 1 , wherein said interaction-insights module comprising:

(i) retrieving a transcript of the interaction based on an interaction-identifier of the interaction,

wherein the transcript is tokenized into one or more sentences, and

wherein each sentence in the one or more sentences is labeled with a start-timestamp and a participant-role,

(ii) constructing a multi-step prompt based on the transcript and one or more polar-questions related to appearance of the evaluation-measurement and the one or more parameters in the transcript, the participant-role, and the start-timestamp;

(iii) executing Artificial Intelligence (AI) models with the multi-step prompt to yield a response, for the evaluation-measurement and the one or more parameters,

wherein the response comprising an answer to each polar-question, the participant-role, and the start-timestamp and

wherein the answer is one of: affirmative-response and negative-response;

for each parameter of the one or more parameters of the evaluation-measurement:

(iv) when the answer is affirmative generating the text-annotation based on the participant-role, the parameter, and a start-timestamp,

(v) when the answer is negative, generating the text-annotation based on the parameter and a provided start-timestamp,

wherein the start-timestamp is provided by a first-step in the multi-step prompt which requires to search the start-timestamp related to absence of the parameter to yield the provided start-timestamp;

(vi) generating a suggestion based on the answer by a second-step in the multi-step prompt and adding the suggestion to the text-annotation; and

(vii) adding the point-in-time annotation,

wherein the point-in-time annotation is added with the start-timestamp as the playhead position in the media-file and the generated text-annotation.

8 . The computerized-method of claim 1 , wherein said computerized-method is further comprising configuring the media-playback service to operate an extract-of-interaction module to generate an abbreviated media-file including one or more sections, each section has a point-in-time annotation, wherein said extract-of-interaction model comprising:

(i) retrieving a transcript of the interaction based on an interaction-identifier of the interaction,

wherein the transcript is tokenized into one or more sentences, and

wherein each sentence in the one or more sentences is labeled with a start-timestamp and a participant-role;

(ii) executing AI models with Large Language Model (LLM) with the transcript and an excerpt-prompt to yield one or more portions of the transcript and an associated chapter-name for each portion,

wherein each portion in the one or more portions has an associated start-timestamp and end-timestamp;

(iii) cutting-out one or more segments from the media-file based on the associated start-timestamp and end-timestamp of each portion;

(iv) combining the one or more segments based on the start-timestamp of each segment to yield the abbreviated media-file; and

(v) creating chapters in the abbreviated media-file by marking each segment in the abbreviated media-file as a chapter and assigning the associated chapter name as a title of the chapter;

and configuring the recording-player web-application to present the abbreviated media-file and the chapter-name of each segment in the abbreviated media-file via the UI.

9 . The computerized-method of claim 8 , wherein the computerized-method is further comprising configuring the recording-player web-application to playback the abbreviated media-file upon a first user-selection of the abbreviated media-file via the UI.

10 . The computerized-method of claim 8 , wherein the computerized-method is further comprising configuring the recording-player web-application to playback the segment upon a second user-selection of the chapter-name via the UI.

11 . The computerized-method of claim 1 , wherein said computerized-method is further comprising configuring the media-playback service to operate an interactive-search module to enable search in the media-file by text-questions via the recording-player web-application, said interactive-search module comprising:

(i) receiving a polar-query in natural language from a user via the UI that is associated to the recording-player web-application;

(ii) retrieving a transcript of the interaction based on an interaction-identifier of the interaction,

wherein the transcript is tokenized into one or more sentences, and

wherein each sentence in the one or more sentences is labeled with a start-timestamp and a participant-role;

(iii) constructing a search-prompt with the transcript, the polar-query in natural language and a request for one or more related start-timestamps embedded therein; and

(iv) executing AI models with LLM with the search-prompt to yield a response and the one or more related start-timestamps,

wherein when the response is affirmative, configuring the recording-player web-application to present an annotation-marker in each start-timestamp of the one or more related start-timestamps, via the UI that is associated to the recording-player web-application, on a timeline-bar, and

wherein when the response is negative, configuring the recording-player web-application to present the response via the UI that is associated to the recording-player web-application.

12 . A computerized-system for reducing time taken for evaluation of an interaction by annotating a media-file of an interaction that has been recorded by a recording-player web-application based on an evaluation-measurement, said computerized-system comprising:

one or more processors, said one or more processors are configured to:

(i) receive a request from a user to playback the media-file of the interaction by operating the media-playback service of the recording-player web-application;

configure the media-playback service to: (ii)

a. operate an interaction-insights module to generate one or more point-in-time annotations of the media-file, based on one or more parameters of the evaluation-measurement; and

b. send the one or more point-in-time annotations and a location of the media-file to the recording-player web-application;

(iii) configure the recording-player web-application to playback the media-file and present each point-in-time annotation of the one or more point-in-time annotations, upon a user-selection, via a User Interface (UI) that is associated to the recording-player web-application, on a timeline-bar as an annotation-marker,

wherein each point-in-time annotation comprising a playhead position in the media-file and a text-annotation related to a parameter of the one or more parameters of the evaluation-measurement.

13 . The computerized-system of claim 12 , wherein the one or more processors are further configured to check and annotate the media-file of the interaction based on one or more evaluation-measurements.

14 . The computerized-method of claim 12 , wherein the evaluation-measurement is one of: (i) customer demography and identification; (ii) agent behavior analysis; (iii) interaction analytics; and (iv) interaction opening and closing,

wherein the one or more parameters of the customer demography and identification is at least one of: a. customer identity verification; b. identify information compromised; and c. customer demographic check,

wherein the one or more parameters of the agent behavior analysis is at least one of: a. empathy; b. politeness,

wherein the one or more parameters of the interaction analytics is at least one of: a. no delays; b. silence; c. long holds; and d. unnecessary holds, and

wherein the one or more parameters of the interaction opening and closing is at least one of: a. greetings at the opening of the interaction; and b. greetings at the closing of the interaction.

15 . The computerized-method of claim 12 , wherein the user-selection is one of: first user-selection of the annotation-marker on the timeline-bar, to display the text-annotation related to the evaluation measurement on the timeline-bar, and second user-selection of all-annotation-markers, to display each text-annotation related to the evaluation measurement of the one or more point-in-time annotations on the timeline-bar.

16 . The computerized-system of claim 15 , wherein the first user-selection and the second user-selection are operated by at least one of: (i) mouse click; (ii) keystroke; (iii) keystroke combination; and (iv) other form of selection.

17 . The computerized-system of claim 15 , wherein each generated point-in-time annotation of the one or more point-in-time annotations of the media-file is stored as an attribute of the interaction in a database.

18 . The computerized-method of claim 12 , wherein said interaction-insights module comprising:

(i) retrieving a transcript of the interaction based on an interaction-identifier of the interaction,

wherein the transcript is tokenized into one or more sentences, and

wherein each sentence in the one or more sentences is labeled with a start-timestamp and a participant-role,

(ii) constructing a multi-step prompt based on the transcript and one or more polar-questions related to appearance of the evaluation-measurement and the one or more parameters in the transcript, the participant-role, and the start-timestamp;

(iii) executing Artificial Intelligence (AI) models with the multi-step prompt to yield a response, for the evaluation-measurement and the one or more parameters,

wherein the response comprising an answer to each polar-question, the participant-role, and the start-timestamp and

wherein the answer is one of: affirmative-response and negative-response;

for each parameter of the one or more parameters of the evaluation-measurement:

(iv) when the answer is affirmative generating the text-annotation based on the participant-role, the parameter, and a start-timestamp;

(v) when the response is negative, generating the text-annotation based on the parameter and a provided start-timestamp,

wherein the start-timestamp is provided by a first-step in the multi-step prompt which requires to search the start-timestamp related to absence of the parameter to yield the provided start-timestamp;

(viii) generating a suggestion based on the answer by a second-step in the multi-step prompt and adding the suggestion to the text-annotation; and

(ix) adding the point-in-time annotation,

wherein the point-in-time annotation is added with the start-timestamp as the playhead position in the media-file and the generated text-annotation.

19 . The computerized-system of claim 12 , wherein said one or more processors are further configured to configure the media-playback service to operate an extract-of-interaction module to generate an abbreviated media-file including one or more sections, each section has a point-in-time annotation, wherein said extract-of-interaction model comprising:

(i) retrieving a transcript of the interaction based on an interaction-identifier of the interaction,

wherein the transcript is tokenized into one or more sentences, and

wherein each sentence in the one or more sentences is labeled with a start-timestamp and a participant-role;

(ii) executing AI models with Large Language Model (LLM) with the transcript and an excerpt-prompt to yield one or more portions of the transcript and an associated chapter-name for each portion,

wherein each portion in the one or more portions has an associated start-timestamp and end-timestamp;

(iii) cutting-out one or more segments from the media-file based on the associated start-timestamp and end-timestamp of each portion;

(iv) combining the one or more segments based on the start-timestamp of each segment to yield the abbreviated media-file; and

(v) creating chapters in the abbreviated media-file by marking each segment in the abbreviated media-file as a chapter and assigning the associated chapter name as a title of the chapter;

and configure the recording-player web-application to present the abbreviated media-file and the chapter-name of each segment in the abbreviated media-file via the UI.

20 . The computerized-method of claim 19 , wherein said one or more processors are further configured to configuring the recording-player web-application to playback the abbreviated media-file upon a first user-selection of the abbreviated media-file via the UI.

21 . The computerized-method of claim 19 , wherein said one or more processors are further configured to configuring the recording-player web-application to playback the segment upon a second user-selection of the chapter-name via the UI.

22 . The computerized-system of claim 12 , wherein said one or more processors are further configured to configure the media-playback service to operate an interactive-search module to enable search in the media-file by text-questions via the recording-player web-application, said interactive-search module comprising:

(i) receiving a polar-query in natural language from a user via the UI that is associated to the recording-player web-application;

(ii) retrieving a transcript of the interaction based on an interaction-identifier of the interaction,

wherein the transcript is tokenized into one or more sentences, and

wherein each sentence in the one or more sentences is labeled with a start-timestamp and a participant-role;

(iii) constructing a search-prompt with the transcript, the polar-query in natural language and a request for one or more related start-timestamps embedded therein;

(iv) executing AI models with LLM with the search-prompt to yield a response and the one or more related start-timestamps,

wherein when the response is affirmative, configuring the recording-player web-application to present an annotation-marker in each start-timestamp of the one or more related start-timestamps, via the UI that is associated to the recording-player web-application, on a timeline-bar, and

wherein when the response is negative, configuring the recording-player web-application to present the response via the UI that is associated to the recording-player web-application.