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 .
Response to Arguments
Applicant's arguments filed 2/25/26 have been fully considered but they are not persuasive.
Regarding the 35 U.S.C. 101 rejection of the claims, Applicant argues that the claims are directed to a computer-implemented method under the control of one or more computer systems configured with executable instructions to fill relevant data fields or electronic forms of a customer relationship management system and are as such undisputedly directed to a statutory category (Arguments, pg.5-6).
Examiner respectfully disagrees as the claims are directed to analyzing, using one or more natural language processing algorithms, at least one source of data that is representative of an interaction between a first party and a second party to extract from the at least one source of data, information that is descriptive of at least part of the interaction (i.e., a data analysis/evaluation step) and uploading information derived from the extracted information to fill data field or electronic forms of a data management system that manages data associated with the first party (i.e., a data analysis/storage step) without significantly more, corresponding to steps achievable by a human in manually transcribing an interaction, analyzing resulting text data, as well as storing results of the analysis. That the steps are computer-implemented (i.e., the use of a generic computer) does not detract from a user manually transcribing a transcribing a call and analyzing resulting text as such steps involve tying an abstract idea to a generic computer implementation.
Applicant also argues that automating and transcribing in the appropriate fields in the place of a human is not a simple ask and it involves a computer program performing the functions faster, and as such, the claims are integrated into a practical application ((Arguments, pg. 6-7).
Examiner respectfully disagrees as a human (including court stenographers, transcribers) can transcribe an interaction while filling out forms with information gleaned from transcriptions of such interaction. That a computer can perform the steps faster does not provide a practical application - “relying on a computer to perform routine tasks more quickly or more accurately is insufficient to render a claim patent eligible.” and the “use of a computer to create electronic records, track multiple
transactions, and issue simultaneous instructions” is not an inventive concept, see “OIP Techs., 788 F.3d at 1363 (citing Alice, 573 U.S. at 224). Also, “merely adding computer functionality to increase the speed or efficiency of the process does not confer patent eligibility on an otherwise abstract idea.” see Intellectual Ventures I LLC, 792 F.3d at 1370.
Applicant further argues that the cited references do not anticipate all the limitations of the since amended independent claims, and as such, argues that the claims recite significantly more (Arguments, pg. 7),
Examiner respectfully disagrees as the novelty or non-obviousness (35 U.S.C. 102/103) of the claims over prior art does not automatically confer patent eligibility in terms of statutory subject matter (35 U.S.C. 101) - “groundbreaking, innovative, or even brilliant discovery does not by itself satisfy the § 101 inquiry.” see Ass’n for Molecular Pathology v. Myriad Genetics, Inc., 569 U.S. 576, 591 (2013)., “A novel and non
obvious claim directed to a purely abstract idea is, nonetheless, patent ineligible” see Mayo, 566 U.S. at 90, “The ‘novelty’ of any element or steps in a process, or even of the process itself, is of no relevance in determining whether the subject matter of a claim falls within the § 101 categories of possibly patentable subject matter” see also Diamond v. Diehr, 450 U.S. 175, 188–89 (1981). Nevertheless, the amended language introduced by claims are rejected with reference Hernandez as provided below
Applicant’s arguments with respect to claims 1, 10 and 18, and as a result, claims dependent therefrom, and reference Sachdev not disclosing "uploading information derived from the extracted information to fill relevant data fields or electronic forms of a data management system" (Arguments, pg. 7-8, sec. III) have been considered but are moot in light of new grounds of rejection with reference Hernandez as presented below.
Regarding the 35 U.S.C. 103 rejection of the claims with additional reference Xiao, Applicant argues that Xiao fails to disclose the limitations recited in the independent claims and as such, does not teach the language recited in the dependent claims (Arguments, pg. 9, sec. IV).
Examiner respectfully disagrees as Xiao was/is not applied to teach language recited in the independent claims, and absent any argument as to why the cited portions of Xiao fail to address language recited in the dependent claims, Examiner maintains that the rejections of the dependent claims are appropriate.
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 1-18 are rejected under 35 U.S.C. 101 because the claimed invention is directed to text/call analysis without significantly more. The claims 1, 10 and 18 recite steps of “under the control of one or more computer systems configured with executable instructions: analyzing, using one or more natural language processing algorithms, at least one source of data that is representative of an interaction between a first party and a second party to extract from the at least one source of data, information that is descriptive of at least part of the interaction (i.e., a data analysis/evaluation step) and uploading information derived from the extracted information to fill data field or electronic forms of a data management system that manages data associated with the first party (i.e., a data analysis/storage step), corresponding to steps achievable by a human in manually transcribing an interaction and analyzing resulting text as well as storing results of the analysis, and as such, the steps correspond to the mental processes category of abstract ideas. This judicial exception is not integrated into a practical application because the claims are directed to an abstract idea with an additional generic computer element, where the generically recited computer element (computer-implemented method, computer systems) do not add a meaningful limitation to the abstract idea because it amounts to simply implementing the abstract idea on a computer. The claims do not include additional elements that are sufficient to amount to significantly more than the judicial exception because step “uploading information derived from the extracted information to a data management system that manages data associated with the first party” corresponds to the well-understood, routine, conventional computer function of storage and retrieval of information to/from memory as recognized by the court decisions listed in MPEP § 2106.05 and as provided by cited references Sachdev and Xiao (see PTO 892 form).
The dependent claims 2-9 and 11-17 also recite mental processes and do not add significantly more than the abstract idea and are as such similarly rejected.
Claim Rejections - 35 USC § 103
In the event the determination of the status of the application as subject to AIA 35 U.S.C. 102 and 103 (or as subject to pre-AIA 35 U.S.C. 102 and 103) is incorrect, any correction of the statutory basis (i.e., changing from AIA to pre-AIA ) for the rejection will not be considered a new ground of rejection if the prior art relied upon, and the rationale supporting the rejection, would be the same under either status.
The following is a quotation of 35 U.S.C. 103 which forms the basis for all obviousness rejections set forth in this Office action:
A patent for a claimed invention may not be obtained, notwithstanding that the claimed invention is not identically disclosed as set forth in section 102, if the differences between the claimed invention and the prior art are such that the claimed invention as a whole would have been obvious before the effective filing date of the claimed invention to a person having ordinary skill in the art to which the claimed invention pertains. Patentability shall not be negated by the manner in which the invention was made.
1. Claims 1-3, 8-12 and 16-18 are rejected under 35 U.S.C. 103 as being unpatentable over Sachdev US 2021/0126999 A1 (“Sachdev”) in view of Hernandez et al US 2025/0265413 A1 (“Hernandez”)
Per claim 1, Sachdev discloses a computer-implemented method comprising:
under the control of one or more computer systems configured with executable instructions: analyzing, using one or more natural language processing algorithms, at least one source of data that is representative of an interaction between a first party and a second party to extract from the at least one source of data, information that is descriptive of at least part of the interaction (A call summarization apparatus comprises a call processing engine, which is implemented on one or more computers. The engine is configured to transcribe audio data from a call when the call is still active, and generate text data of the call in real time, that is, as the parties on the call (i.e., a customer and an agent) speak…., para. [0011]; Using Natural Language Processing (NLP) techniques with built-in Natural Language Understanding & Generation (NLU, NLG) techniques, powered by deep, bi-directional Recursive Neural Networks (RNN) algorithm, the engine identifies the intents in the call, extracts various slots under each intent to further identify the sentiments, resolutions, actions and promises from the call. Intents are conversation shapers, and the issue is one of the intents…., para. [0012], transcription as source of data); and
uploading information derived from the extracted information to a data management system that manages data associated with the first party (para. [0012]; A final version of the call summary 104 may is stored in the database 120 for future reference, para. [0023]; The method 200 proceeds to optional step 290, at which the summarization module 118 includes data generated in steps 240-270, or portions thereof in the summary 104. For example, such data includes the intent of the customer, a slot of the intent, a sentiment of the customer …, para. [0028])
Sachdev does not explicitly disclose uploading information derived from the extracted information to fill relevant data fields or electronic forms of a data management system that manages data associated with the first party
However, this feature is taught by Hernandez (an automated context monitoring system may utilize server hosts chat interfaces to monitor chats during which customers communicate with agent-advisors (“agents”) for online support. The automated context monitoring system can generate a summary of the chat session as it progresses and execute a machine learning (ML) model (e.g., a large language model and/or the like) to perform various operations. For instance, the large language model can help identify the concepts discussed with the customer and prepare a summary for the agent's review …, para. [0014]; para. [0018]; [0028]; the automated context monitoring system may parse the plurality of form files to determine the set of fields, where each field has a name and corresponds to a type of information exchanged during a chat session. In some embodiments, the automated context monitoring system may identify instances of field names that correspond to field names included in the summary. For example, the automated context monitoring system may identify the instances of filed names in a form file corresponding to fields in the summary and copy the information from the summary into the form file, para. [0050]-[0052], copying the information from call summary into fields of form file as uploading)
It would have been obvious to one of ordinary skill in the art to combine the teachings of Hernandez with the method of Sachdev in arriving at the missing features of Sachdev, because such combination would have resulted in increasing the timeliness and accuracy of chat sessions (Hernandez, para. [0016])
Per claim 2, Sachdev in view of Hernandez discloses the computer-implemented method of claim 1,
Sachdev discloses wherein the at least one source of data includes at least one of linguistic data or textual data (para. [0011]-[0012]).
Per claim 3, Sachdev in view of Hernandez discloses the computer-implemented method of claim 1,
Sachdev discloses wherein the at least one source of data is a transcript of at least part of the interaction (para. [0011]-[0012]).
Per claim 8, Sachdev in view of Hernandez discloses the computer-implemented method of claim 1,
Sachdev discloses wherein the information derived from the extracted information includes at least one insight of the interaction (para. [0012]; para. [0028]).
Per claim 9, Sachdev in view of Hernandez discloses the computer-implemented method of claim 1,
Sachdev discloses modifying, by the second party, the extracted information to produce the information derived from the extracted information, prior to the information derived from the extracted information being uploaded to the system (para. [0012]; the call summary 104 is editable. The agent may edit the call summary 104 from within the call summary section of the GUI. A final version of the call summary 104 may is stored in the database 120 …, para. [0023]).
Per claim 10, Sachdev discloses a computer-implemented method comprising:
under the control of one or more computer systems configured with executable instructions: analyzing, using one or more natural language processing algorithms, at least one source of data that is representative of an interaction between a customer and a customer service representative to extract from the at least one source of data insight data that is descriptive of at least one insight of the interaction (A call summarization apparatus comprises a call processing engine, which is implemented on one or more computers. The engine is configured to transcribe audio data from a call when the call is still active, and generate text data of the call in real time, that is, as the parties on the call (i.e., a customer and an agent) speak…., para. [0011]; Using Natural Language Processing (NLP) techniques with built-in Natural Language Understanding & Generation (NLU, NLG) techniques, powered by deep, bi-directional Recursive Neural Networks (RNN) algorithm, the engine identifies the intents in the call, extracts various slots under each intent to further identify the sentiments, resolutions, actions and promises from the call. Intents are conversation shapers, and the issue is one of the intents…., para. [0012], transcription as source of data); and
uploading the insight data to a customer relationship management system that manages data associated with the customer and that is managed by an organization that includes the customer service representative (para. [0012]; A final version of the call summary 104 may is stored in the database 120 for future reference, para. [0023]; The method 200 proceeds to optional step 290, at which the summarization module 118 includes data generated in steps 240-270, or portions thereof in the summary 104. For example, such data includes the intent of the customer, a slot of the intent, a sentiment of the customer …, para. [0028])
Sachdev does not explicitly disclose uploading the insight data to fill relevant data fields or electronic forms of a customer relationship management system that manages data associated with the customer and that is managed by an organization that includes the customer service representative
However, this feature is taught by Hernandez (an automated context monitoring system may utilize server hosts chat interfaces to monitor chats during which customers communicate with agent-advisors (“agents”) for online support. The automated context monitoring system can generate a summary of the chat session as it progresses and execute a machine learning (ML) model (e.g., a large language model and/or the like) to perform various operations. For instance, the large language model can help identify the concepts discussed with the customer and prepare a summary for the agent's review …, para. [0014]; para. [0018]; [0028]; the automated context monitoring system may parse the plurality of form files to determine the set of fields, where each field has a name and corresponds to a type of information exchanged during a chat session. In some embodiments, the automated context monitoring system may identify instances of field names that correspond to field names included in the summary. For example, the automated context monitoring system may identify the instances of filed names in a form file corresponding to fields in the summary and copy the information from the summary into the form file, para. [0050]-[0052], copying the information from call summary into fields of form file as uploading)
It would have been obvious to one of ordinary skill in the art to combine the teachings of Hernandez with the method of Sachdev in arriving at the missing features of Sachdev, because such combination would have resulted in increasing the timeliness and accuracy of chat sessions (Hernandez, para. [0016]).
Per claim 11, Sachdev in view of Hernandez discloses the computer-implemented method of claim 10,
Sachdev discloses wherein the at least one source of data includes at least one of linguistic data or textual data (para. [0011]-[0012]).
Per claim 12, Sachdev in view of Hernandez discloses the computer-implemented method of claim 10,
Sachdev discloses wherein the at least one source of data is a transcript of the interaction (para. [0011]-[0012]).
Per claim 16, Sachdev in view of Hernandez discloses the computer-implemented method of claim 10,
Sachdev discloses modifying, by the customer service representative, the insight data prior to the insight data being uploaded to the customer relationship management system (para. [0012]; the call summary 104 is editable. The agent may edit the call summary 104 from within the call summary section of the GUI. A final version of the call summary 104 may is stored in the database 120 …, para. [0023]).
Per claim 17, Sachdev in view of Hernandez discloses the computer-implemented method of claim 10,
Sachdev discloses: wherein the at least one source of data includes a plurality of sources of data, each source of data corresponding to a different respective aspect of the interaction (The transcribing module 112 receives audio data of the call 102 in real-time. The transcribing module 112 transcribes the audio data to generate text data corresponding to the conversation between the caller or customer and the agent who receives the call …, para. [0016]), and
wherein the insight data includes a plurality of sets of data elements, each set of data elements being descriptive of a respective insight of a plurality of insights, each insight of the plurality of insights being associated with a different respective one of the aspects (At step 282 of the method 200, the summarization module 118 identifies an issue of the customer based on at least one of a call transcript, an extracted intent from the call transcript, a slot of the intent, and/or a sentiment of the customer …, para. [0028])
the method further comprising: aggregating the data elements to produce an aggregated data element that is a representation of an aggregation of the plurality of insights (The method 200 proceeds to optional step 290, at which the summarization module 118 includes data generated in steps 240-270, or portions thereof in the summary 104. For example, such data includes the intent of the customer, a slot of the intent, a sentiment of the customer …, para. [0028]).
Per claim 18, Sachdev discloses a computer-implemented method comprising:
under the control of one or more computer systems configured with executable instructions: analyzing, using one or more natural language processing algorithms, at least one source of data that is representative of an interaction between a customer of an organization and a customer service representative that is a member of the organization to extract from the at least one source of data information that is descriptive of at least part of the interaction (A call summarization apparatus comprises a call processing engine, which is implemented on one or more computers. The engine is configured to transcribe audio data from a call when the call is still active, and generate text data of the call in real time, that is, as the parties on the call (i.e., a customer and an agent) speak…., para. [0011]; Using Natural Language Processing (NLP) techniques with built-in Natural Language Understanding & Generation (NLU, NLG) techniques, powered by deep, bi-directional Recursive Neural Networks (RNN) algorithm, the engine identifies the intents in the call, extracts various slots under each intent to further identify the sentiments, resolutions, actions and promises from the call. Intents are conversation shapers, and the issue is one of the intents…., para. [0012], transcription as source of data); and
uploading information derived from the extracted information to a customer relationship management system that manages data associated with the customer and that is managed by the organization (para. [0012]; A final version of the call summary 104 may is stored in the database 120 for future reference, para. [0023]; The method 200 proceeds to optional step 290, at which the summarization module 118 includes data generated in steps 240-270, or portions thereof in the summary 104. For example, such data includes the intent of the customer, a slot of the intent, a sentiment of the customer …, para. [0028])
Sachdev does not explicitly disclose uploading information derived from the extracted information to fill relevant data fields or electronic forms of a customer relationship management system that manages data associated with the customer and that is managed by the organization
However, this feature is taught by Hernandez (an automated context monitoring system may utilize server hosts chat interfaces to monitor chats during which customers communicate with agent-advisors (“agents”) for online support. The automated context monitoring system can generate a summary of the chat session as it progresses and execute a machine learning (ML) model (e.g., a large language model and/or the like) to perform various operations. For instance, the large language model can help identify the concepts discussed with the customer and prepare a summary for the agent's review …, para. [0014]; para. [0018]; [0028]; the automated context monitoring system may parse the plurality of form files to determine the set of fields, where each field has a name and corresponds to a type of information exchanged during a chat session. In some embodiments, the automated context monitoring system may identify instances of field names that correspond to field names included in the summary. For example, the automated context monitoring system may identify the instances of filed names in a form file corresponding to fields in the summary and copy the information from the summary into the form file, para. [0050]-[0052], copying the information from call summary into fields of form file as uploading)
It would have been obvious to one of ordinary skill in the art to combine the teachings of Hernandez with the method of Sachdev in arriving at the missing features of Sachdev, because such combination would have resulted in increasing the timeliness and accuracy of chat sessions (Hernandez, para. [0016]).
2. Claims 4-7 and 13-15 are rejected under 35 U.S.C. 103 as being unpatentable over Sachdev in view of Hernandez as applied to claims 1 and 10 above, and further in view of Xiao et al US 12,334,063 B1 (“Xiao”)
Per claim 4, Sachdev in view of Hernandez discloses the computer-implemented method of claim 1,
Sachdev does not explicitly disclose wherein the analyzing the at least one source of data includes transcribing the at least one source of data into text to produce a transcript of at least part of the interaction
However, this feature is taught by Xiao (col. 2, ln 30-34; an AI or ML-driven feature generates a summary note which includes one or more of a customer issue, a predicted outcome of the agent's actions, agent resolution steps and follow-up actions in human-like language after being fed a conversation transcript from an Automatic Speech Recognition (“ASR”) system for a vocal call (or chat history, for an online chat session). …, col. 3, ln 22-28)
It would have been obvious to one of ordinary skill in the art to combine the teachings of Xiao with the method of Sachdev in view of Hernandez in arriving at the missing features of Sachdev in view of Hernandez, because such combination would have resulted in providing alternate forms of contact with consumers or other parties (Xiao, col. 2, ln 8-34).
Per claim 5, Sachdev in view of Hernandez and Xiao discloses the computer-implemented method of claim 4,
Xiao discloses wherein the analyzing the at least one source of data further includes parsing the transcript into one or more transcript sections (Logic applications and conditions can break the full transcript block of text into smaller pieces. For example, a predefined period of silence or paused talking can be interpreted as an unimportant conversation break …, col. 5, ln 12-18)
Per claim 6, Sachdev in view of Hernandez and Xiao discloses the computer-implemented method of claim 5,
Xiao discloses wherein the analyzing the at least one source of data further includes classifying the one or more transcript sections according to a plurality of classification labels (col. 5, ln 12 – col. 6, ln 14)
Per claim 7, Sachdev in view of Hernandez discloses the computer-implemented method of claim 1,
Sachdev does not explicitly disclose wherein the at least one source of data is received from a computer memory
However, this feature is taught by Xiao (col. 3, ln 22-28; col. 5, ln 8-18; A transcript of the communication can be generated or obtained 408, either in real-time or following conclusion of the discussion.…, col. 9, ln 6-8)
It would have been obvious to one of ordinary skill in the art to combine the teachings of Xiao with the method of Sachdev in view of Hernandez in arriving at the missing features of Sachdev in view of Hernandez, because such combination would have resulted in providing away to process received user requests (Xiao, col. 4, ln 37-64).
Per claim 13, Sachdev in view of Hernandez discloses the computer-implemented method of claim 10,
Sachdev does not explicitly disclose wherein the analyzing the at least one source of data includes transcribing the at least one source of data into text
However, this feature is taught by Xiao (col. 2, ln 30-34; an AI or ML-driven feature generates a summary note which includes one or more of a customer issue, a predicted outcome of the agent's actions, agent resolution steps and follow-up actions in human-like language after being fed a conversation transcript from an Automatic Speech Recognition (“ASR”) system for a vocal call (or chat history, for an online chat session). …, col. 3, ln 22-28)
It would have been obvious to one of ordinary skill in the art to combine the teachings of Xiao with the method of Sachdev in view of Hernandez in arriving at the missing features of Sachdev in view of Hernandez, because such combination would have resulted in providing alternate forms of contact with consumers or other parties (Xiao, col. 2, ln 8-34).
Per claim 14, Sachdev in view of Hernandez discloses the computer-implemented method of claim 10,
Sachdev does not explicitly disclose wherein each insight of the at least one insight is an insight in a category from a set of predetermined categories
However, this feature is taught by Xiao (col. 5, ln 12 – col. 6, ln 14)
It would have been obvious to one of ordinary skill in the art to combine the teachings of Xiao with the method of Sachdev in view of Hernandez in arriving at the missing features of Sachdev in view of Hernandez, because such combination would have resulted in providing speaker/topic identification (Xiao, col. 5, ln 12 – col. 6, ln 14)
Per claim 15, Sachdev in view of Hernandez and Xiao discloses the computer-implemented method of claim 14,
Xiao discloses wherein the set of predetermined categories includes: questions and answers on topics in predefined conversation points of interest of the interaction, and indications that questions or answers were provided by the customer or the customer service representative (Logic applications and conditions can break the full transcript block of text into smaller pieces. For example, a predefined period of silence or paused talking can be interpreted as an unimportant conversation break or change in speaker.… As discussed and/or is apparent from the illustration, the locator 202 can be configured to identify which conversation turn(s) and span(s) in a given conversation transcript contain the issue(s) 208 at hand, one or more possible outcomes 210, and/or one or more resolutions/next steps/action items 212…. Analyses of each conversation turn, round, or span can be conducted independently, or, given that context can be helpful, multiple (or all) rounds may be examined as a whole, such as in a question-and-answer situation. Along these lines, the locator 202 can provide a context for conversation terms and phrases …, col. 5, ln 12 – col. 6, ln 26; col. 7, ln 59 – col. 8, ln 7).
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure. See PTO 892 form.
Applicant's amendment necessitated the new ground(s) of rejection presented in this Office action. Accordingly, THIS ACTION IS MADE FINAL. See MPEP § 706.07(a). Applicant is reminded of the extension of time policy as set forth in 37 CFR 1.136(a).
A shortened statutory period for reply to this final action is set to expire THREE MONTHS from the mailing date of this action. In the event a first reply is filed within TWO MONTHS of the mailing date of this final action and the advisory action is not mailed until after the end of the THREE-MONTH shortened statutory period, then the shortened statutory period will expire on the date the advisory action is mailed, and any nonprovisional extension fee (37 CFR 1.17(a)) pursuant to 37 CFR 1.136(a) will be calculated from the mailing date of the advisory action. In no event, however, will the statutory period for reply expire later than SIX MONTHS from the mailing date of this final action.
Any inquiry concerning this communication or earlier communications from the examiner should be directed to OLUJIMI A ADESANYA whose telephone number is (571)270-3307. The examiner can normally be reached Monday-Friday 8:30-5:00pm.
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/OLUJIMI A ADESANYA/Primary Examiner, Art Unit 2658