DETAILED ACTION
Notice of Pre-AIA or AIA Status
The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA .
Information Disclosure Statement
The information disclosure statements (IDS) submitted on 07/23/2024 and 10/28/2025 are in compliance with the provisions of 37 CFR 1.97. Accordingly, the information disclosure statements are being considered by the examiner.
Drawings
The drawings were submitted on 07/23/2024. These drawings are reviewed and accepted by the examiner.
Claim Rejections - 35 USC § 102
The following is a quotation of the appropriate paragraphs of 35 U.S.C. 102 that form the basis for the rejections under this section made in this Office action:
A person shall be entitled to a patent unless –
(a)(2) the claimed invention was described in a patent issued under section 151, or in an application for patent published or deemed published under section 122(b), in which the patent or application, as the case may be, names another inventor and was effectively filed before the effective filing date of the claimed invention.
Claim(s) 1-9 are rejected under 35 U.S.C. 102(a)(2) as being anticipated by Soini (US 20210397311 A1).
Regarding claims 1, 8, and 9, Soini teaches:
“An information processing device comprising: a processor; and a memory having instructions stored therein that, when executed by the processor” (par. 0038; ‘The modules or engines 302 can be implemented with any combination of software (e.g., executable instructions, or computer code) and hardware (e.g., at least a memory and processor).’), cause the processor to:
“detect a character sequence from text, wherein the text represents content of talk by a plurality of parties, and the plurality of parties comprises a first speaker and a second speaker” (par. 0014; ‘In one example, the live multi-channel audio stream is a live two-channel voice conversation over a telecommunications network between a customer and a customer service agent (“agent”) of a company.’; par. 0015; ‘The backend system performs natural language analysis on the dialogue to extract keywords or other speech features (e.g., indication of a problem) useful for querying a solutions database.’) and
“display, based on the detected character sequence, a display component to the second speaker, wherein the display component receives input and causes executing a search operation to search for answer information to an inquiry from the first user, and the search operation comprises searching for a knowledge that represents the answer information using the character sequence as a search keyword” (par. 0015; ‘The backend search system dynamically adapts the desktop to the current speech features of the conversation, to provide the agent with timely and relevant talking points and answers.’ ‘The system then generates control signals based on search results to dynamically adapt the agent's desktop to the latest live dialogue so that information most likely to be useful to the agent in solving the customer's issues is presented.’; par. 0032; ‘Examples of sources of search results include databases for knowledge base (KB) articles 210, user profiles 212, and tasks or actions 214.’).
Regarding claim 2 (dep. on claim 1), Soini teaches:
“wherein the search operation further comprises searching for, in response to interactively receiving a selection of the display component by the second speaker, the knowledge in a database by using the character sequence corresponding to the display component as the search keyword” (par. 0033; ‘Examples of the input parameters include clicks or other interactions on the agent's desktop in response to search results, events that occurred on the agent's desktop, results that were selected or utilized to address a customer's inquiry, or any other analytic process or operation.’), and
“wherein the instructions, when executed by the processor, further cause the processor to present the knowledge as the answer information to the second speaker” (par. 0049; ‘In 412 the system generates one or more control signals based on the search results. The control signals are configured to control the content and placement of the content (or other resources) on the dynamic desktop during the live audio dialogue.’).
Regarding claim 3 (dep. on claim 1), Soini teaches:
“wherein the instructions, when executed by the processor, further cause the processor to present display components to the second speaker in a sequence of detecting respective character sequences, wherein each of the display components is operable to cause searching for the knowledge by using one of the respective character sequences as the search keyword” (par. 0032; ‘Examples of sources of search results include databases for knowledge base (KB) articles 210, user profiles 212, and tasks or actions 214.’; par. 0048; ‘The system can cause display on the desktop of any of the multiple content items that exceed a first threshold and cause any of the multiple software tools that exceed a second threshold to launch on the desktop.’).
Regarding claim 4 (dep. on claim 3), Soini teaches:
“wherein the instructions, when executed by the processor, further cause the processor to present, the display components when a first character sequence that is identical to a second character sequence that has been detected in past is detected, wherein each of the display components is operable to cause the knowledge to be searched for by using the first character sequence as the search keyword is positioned at a top in an order of the display components presented, or positioned a predetermined number of display components higher in the order of the display components presented” (par. 0039; ‘The analytics module 304-2 can be embodied as an engine that processes inputs (e.g., speech, computer interactions) and outputs the discovery, interpretation, and communication of meaningful patterns. It can also entail applying data patterns towards effective decision making.’; par. 0047; ‘In some implementations, the search results are weighted based on demographic and historical information about the customer and real-time actions performed by the agent on the desktop while engaged in the audio dialogue with the customer.’).
Regarding claim 5 (dep. on claim 1), Soini teaches:
“wherein the instructions, when executed by the processor, further cause the processor to specify context of the content of talk, and present display components to the second speaker in an order of strength of relationship that character sequences hold to the specified context” (par. 0018; ‘The customer's responses can include background or context that can be used to initially structure the tabs of the desktop, including the number of tabs, their order, and content. The windows of tabs with more relevant content are placed toward the front.’; par. 0039; ‘The rules engine 304-3 performs logic-based determinations regarding how to adapt a dynamic desktop, the content items, software tools, notifications, and their placement on the dynamic desktop. For example, the rules engine 304-3 can determine whether the relevance of content items or software tools are shown or launched, respectively, on the dynamic desktop depending on whether their relevance exceeds one or more thresholds.’).
Regarding claim 6 (dep. on claim 1), Soini teaches:
“wherein the display operation further comprises displaying the display components to the second speaker by changing color or shape, or by changing both color and shape, of each display component depending on what category a character sequence belongs to” (par. 0018; ‘The customer's responses can include background or context that can be used to initially structure the tabs of the desktop, including the number of tabs, their order, and content. The windows of tabs with more relevant content are placed toward the front. The order, structure, and content can adapt to the spontaneous dialogue of the live call.’).
Regarding claim 7 (dep. on claim 1), Soini teaches:
“wherein the display operation further comprises displaying the display components to the second speaker by associating the display components with character sequences based on a graph model or a layers model” (par. 0016; ‘For example, the transcribed speech can be used to generate feedback signals that train a machine learning model of the search engine, which can improve the probability of identifying relevant information, though any personally identifiable information related to the customer is not stored.’).
Conclusion
Other pertinent prior art are cited in the PTO-892 for the applicant's consideration.
Any inquiry concerning this communication or earlier communications from the examiner should be directed to MARK VILLENA whose telephone number is (571)270-3191. The examiner can normally be reached 10 am - 6pm EST Monday through Friday.
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MARK . VILLENA
Examiner
Art Unit 2658
/MARK VILLENA/Examiner, Art Unit 2658