DETAILED ACTION
This communication is in response to the Amendments and Arguments filed on 03/30/2026.
Claim(s) 1-20 are pending and have been examined. Hence, this action has been made FINAL.
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 and Amendments
Amendments to the claims by the Applicant have been considered and addressed below.
With respect to the 35 USC § 101 and 103 rejections, the Applicant provides several arguments in which the Examiner will respond accordingly, below.
35 USC § 103 rejection(s)
Arguments in pages 7-9 of remarks filed on 03/30/2026.
Examiner’s Response to Arguments:
Applicant’s arguments with respect to independent claim(s) 1, 15, and 20 under 35 U.S.C. § 103 have been considered but are moot because the new ground of rejection does not rely on any reference applied in the prior rejection of record for any teaching or matter specifically challenged in the argument.
Therefore, the rejection has been withdrawn. However, upon further consideration, a new ground(s) of rejection is made in view of Trzyna (US 20240338860 A1) further in view of Miller (US 11057519 B1) and Callegari et al. (US 20240362422 A1)
For more details, please refer to updated 35 U.S.C. § 103 rejections for claims 1-20 below.
35 USC § 101 rejection(s)
Arguments in pages 9-10 of remarks filed on 03/30/2026.
Examiner response to Arguments:
Applicant’s arguments, with respect to the rejection(s) of independent claim(s) 1, 15, and 20 under 35 USC 101 have been fully considered but are not persuasive.
The Applicant argues that:
The amended transcription step produces a structured data artifact with two properties no human can replicate: speaker tags attributing each portion of the transcript to a specific entity, and time tags synchronizing the text to the audio recording. Generating these elements requires the machine to simultaneously perform speech recognition, speaker diarization, and temporal alignment. No person with pen and paper can perform these three computational processes in parallel. The amended transcription step is therefore not a mental process.
However, the Examiner respectfully disagrees because nowhere in the claim language there is a disclosure of processing in parallel or performing simultaneously speech recognition, speaker diarization, and temporal alignment as argued.
More details on the rationale used to examine the claims rejected under 35 U.S.C. § 101 of the Instant Application are provided below for clarification.
Please see detailed analysis below (Prong Two) for more details on how the Examiner understands the independent claims do not recite additional elements that integrate the judicial exception into a practical application. Hence, not qualifying as patent eligible subject matter under 35 U.S.C. § 101.
Please refer to MPEP 2106.04(II): Eligibility Step 2A: Whether a Claim is Directed to a Judicial Exception: (A) Step 2A is a Two-Prong Inquiry:
(1) Prong One:
Prong One asks does the claim recite an abstract idea, law of nature, or natural phenomenon? In Prong One examiners evaluate whether the claim recites a judicial exception, i.e. whether a law of nature, natural phenomenon, or abstract idea is set forth or described in the claim. While the terms "set forth" and "described" are thus both equated with "recite", their different language is intended to indicate that there are two ways in which an exception can be recited in a claim. For instance, the claims in Diehr, 450 U.S. at 178 n. 2, 179 n.5, 191-92, 209 USPQ at 4-5 (1981), clearly stated a mathematical equation in the repetitively calculating step, and the claims in Mayo, 566 U.S. 66, 75-77, 101 USPQ2d 1961, 1967-68 (2012), clearly stated laws of nature in the wherein clause, such that the claims "set forth" an identifiable judicial exception. Alternatively, the claims in Alice Corp., 573 U.S. at 218, 110 USPQ2d at 1982, described the concept of intermediated settlement without ever explicitly using the words "intermediated" or "settlement." […]
An example of a claim that recites a judicial exception is "A machine comprising elements that operate in accordance with F=ma." This claim sets forth the principle that force equals mass times acceleration (F=ma) and therefore recites a law of nature exception. Because F=ma represents a mathematical formula, the claim could alternatively be considered as reciting an abstract idea. Because this claim recites a judicial exception, it requires further analysis in Prong Two in order to answer the Step 2A inquiry. An example of a claim that merely involves, or is based on, an exception is a claim to "A teeter-totter comprising an elongated member pivotably attached to a base member, having seats and handles attached at opposing sides of the elongated member." This claim is based on the concept of a lever pivoting on a fulcrum, which involves the natural principles of mechanical advantage and the law of the lever. However, this claim does not recite these natural principles and therefore is not directed to a judicial exception (Step 2A: NO). Thus, the claim is eligible at Pathway B without further analysis.
From this analysis, in Step 2A, Prong One, the Examiner has evaluated the independent claims accordingly and determined that the amended independent claims as drafted indeed describe a judicial exception (i.e., an abstract idea), which represent a mental process (which can be performed by a human with pen and paper).
Similar to what was discussed in the Non-Final Rejection mailed on 12/29/2025, the limitations as drafted cover a human (mental process and/or mathematical concept).
More specifically, the claim recitations of:
The independent claim(s) recite(s):
1. A computer-implemented method for processing an audio recording of an interaction, the computer-implemented method comprising:
obtaining the audio recording;
automatically transcribing the audio recording into computer-readable text, wherein:
the computer-readable text includes a set of speaker tags and a set of time tags,
each speaker tag of the set of speaker tags identifies, for a respective portion of the computer-readable text, which entity of the interaction produced the respective portion, and
the set of time tags is configured to synchronize the computer- readable text to the audio recording;
determining a set of artificial intelligence (Al) prompts corresponding to the interaction, wherein each prompt of the set of Al prompts corresponds to a respective compliance rule applicable to the interaction;
providing the computer-readable text and the set of AI prompts to an AI engine;
receiving a set of analyses from the AI engine corresponding to the set of AI prompts;
determining a feature of merit for the interaction based on the set of analyses; and
in response to the feature of merit, selectively generating a notification and transmitting the notification using a selected communications channel.
15. A computer system comprising:
memory hardware configured to store instructions, and
processing hardware configured to execute the instructions, wherein the instructions include:
[the limitations as in claim 1, above].
20. A non-transitory computer-readable medium comprising processor-executable instructions that include:
[the limitations as in claim 1, above].
Read on a human (e.g., mentally and/or using pen and paper):
Receiving/listening to audio;
Transcribing (into text) the speech present in said audio;
Including tags or labels in said transcription (e.g., who said what and at what time)
Determining prompts/sentences from the speech in said audio, wherein the prompts are associated with predetermined rules;
Using said prompts/sentences to apply a predetermined set of rules;
Writing down an analysis for each of the prompts/sentences according to the predetermined set of rules;
Assigning a value to the interactions from the speech/audio;
Writing down on a piece of paper a selection or result based on the assigned value(s).
Please also refer to MPEP 2106.05(f)(2): Whether the claim invokes computers or other machinery merely as a tool to perform an existing process, and MPEP 2106.06(b): Clear Improvement to a Technology or to Computer Functionality.
Please refer to MPEP 2106.04(II): Eligibility Step 2A: Whether a Claim is Directed to a Judicial Exception: (A) Step 2A is a Two-Prong Inquiry:
(2) Prong Two:
Prong Two asks does the claim recite additional elements that integrate the judicial exception into a practical application? In Prong Two, examiners evaluate whether the claim as a whole integrates the exception into a practical application of that exception. If the additional elements in the claim integrate the recited exception into a practical application of the exception, then the claim is not directed to the judicial exception (Step 2A: NO) and thus is eligible at Pathway B. This concludes the eligibility analysis. If, however, the additional elements do not integrate the exception into a practical application, then the claim is directed to the recited judicial exception (Step 2A: YES), and requires further analysis under Step 2B (where it may still be eligible if it amounts to an ‘‘inventive concept’’). For more information on how to evaluate whether a judicial exception is integrated into a practical application, see MPEP § 2106.04(d)(2).
From this analysis, in Step 2A, Prong Two, the Examiner has evaluated the independent claims accordingly and determined that the amended independent claims as drafted that the claims as a whole do not include additional elements that integrate the exception into a practical application of that exception. (i.e., an abstract idea). As discussed in the Non-Final Rejection mailed on 12/29/2026:
This judicial exception is not integrated into a practical application because for example: independent claims 1, 15, 20 recite “a computer-implemented method,” “computer-readable text,” “a computer system,” “memory hardware,” and/or “processing hardware”. As an example, in ¶ [0085] of the as filed specification, “The apparatuses and methods described in this application may be partially or fully implemented by a special-purpose computer created by configuring a general-purpose computer to execute one or more particular functions embodied in computer programs. Such apparatuses and methods may be described as computerized or computer-implemented apparatuses and methods.”. Therefore, a general-purpose computer or computing device is described and mainly used as an application thereof. Accordingly, these additional elements do not integrate the abstract idea into a practical idea because it does not impose any meaningful limits on practicing the abstract idea.
Please also refer to MPEP 2106.05(f)(2): Whether the claim invokes computers or other machinery merely as a tool to perform an existing process.
Finally, please refer to MPEP 2106.05(A): Relevant Considerations For Evaluating Whether Additional Elements Amount To An Inventive Concept
Limitations that the courts have found not to be enough to qualify as "significantly more" when recited in a claim with a judicial exception include:
i. Adding the words "apply it" (or an equivalent) with the judicial exception, or mere instructions to implement an abstract idea on a computer, e.g., a limitation indicating that a particular function such as creating and maintaining electronic records is performed by a computer, as discussed in Alice Corp., 573 U.S. at 225-26, 110 USPQ2d at 1984 (see MPEP § 2106.05(f));
ii. Simply appending well-understood, routine, conventional activities previously known to the industry, specified at a high level of generality, to the judicial exception, e.g., a claim to an abstract idea requiring no more than a generic computer to perform generic computer functions that are well-understood, routine and conventional activities previously known to the industry, as discussed in Alice Corp., 573 U.S. at 225, 110 USPQ2d at 1984 (see MPEP § 2106.05(d));
From this analysis, in Step 2B, the Examiner has evaluated the independent claims accordingly and determined that the independent claims as drafted have limitations that the courts have found not to be enough to qualify as "significantly more" when recited in a claim with a judicial exception. Similar to what was discussed in the Non-Final Rejection mailed on 12/29/2025:
The claim(s) does/do not include additional elements that are sufficient to amount to significantly more than the judicial exception. As discussed above with respect to the integration of the abstract idea into a practical application, the additional elements of using a computer is listed as a general computing device as noted. The claim is not patent eligible.
For more details, please refer to updated 35 U.S.C. § 101 rejections for claims 1-20 below.
Specification
The lengthy specification has not been checked to the extent necessary to determine the presence of all possible minor errors. Applicant’s cooperation is requested in correcting any errors of which applicant may become aware in the specification.
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.
Claim(s) 1-20 rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more. More specifically directed to the abstract idea grouping of: mental process.
The independent claim(s) recite(s):
1. A computer-implemented method for processing an audio recording of an interaction, the computer-implemented method comprising:
obtaining the audio recording;
automatically transcribing the audio recording into computer-readable text, wherein:
the computer-readable text includes a set of speaker tags and a set of time tags,
each speaker tag of the set of speaker tags identifies, for a respective portion of the computer-readable text, which entity of the interaction produced the respective portion, and
the set of time tags is configured to synchronize the computer- readable text to the audio recording;
determining a set of artificial intelligence (Al) prompts corresponding to the interaction, wherein each prompt of the set of Al prompts corresponds to a respective compliance rule applicable to the interaction;
providing the computer-readable text and the set of AI prompts to an AI engine;
receiving a set of analyses from the AI engine corresponding to the set of AI prompts;
determining a feature of merit for the interaction based on the set of analyses; and
in response to the feature of merit, selectively generating a notification and transmitting the notification using a selected communications channel.
15. A computer system comprising:
memory hardware configured to store instructions, and
processing hardware configured to execute the instructions, wherein the instructions include:
[the limitations as in claim 1, above].
20. A non-transitory computer-readable medium comprising processor-executable instructions that include:
[the limitations as in claim 1, above].
This reads on a human (e.g., mentally and/or using pen and paper):
Receiving/listening to audio;
Transcribing (into text) the speech present in said audio;
Including tags or labels in said transcription (e.g., who said what and at what time)
Determining prompts/sentences from the speech in said audio, wherein the prompts are associated with predetermined rules;
Using said prompts/sentences to apply a predetermined set of rules;
Writing down an analysis for each of the prompts/sentences according to the predetermined set of rules;
Assigning a value to the interactions from the speech/audio;
Writing down on a piece of paper a selection or result based on the assigned value(s).
This judicial exception is not integrated into a practical application because for example: independent claims 1, 15, 20 recite “a computer-implemented method,” “computer-readable text,” “a computer system,” “memory hardware,” and/or “processing hardware”. As an example, in ¶ [0085] of the as filed specification, “The apparatuses and methods described in this application may be partially or fully implemented by a special-purpose computer created by configuring a general-purpose computer to execute one or more particular functions embodied in computer programs. Such apparatuses and methods may be described as computerized or computer-implemented apparatuses and methods.”. Therefore, a general-purpose computer or computing device is described and mainly used as an application thereof. Accordingly, these additional elements do not integrate the abstract idea into a practical idea because it does not impose any meaningful limits on practicing the abstract idea.
The claim(s) does/do not include additional elements that are sufficient to amount to significantly more than the judicial exception. As discussed above with respect to the integration of the abstract idea into a practical application, the additional elements of using a computer is listed as a general computing device as noted. The claim is not patent eligible.
With respect to claim 2, the claim(s) recite:
2. The computer-implemented method of claim 1 wherein the audio recording includes a compressed audio file.
This reads on a human (e.g., mentally and/or using pen and paper):
Listening to an audio with predetermined characteristics (i.e., compressed/filtered).
No additional limitations are present.
With respect to claim 3, the claim(s) recite:
3. The computer-implemented method of claim 1 wherein the set of AI prompts is universal to all interactions.
This reads on a human (e.g., mentally and/or using pen and paper):
Working with prompts/sentences that are in a predefined format.
No additional limitations are present.
With respect to claim 4, the claim(s) recite:
4. The computer-implemented method of claim 1 wherein each prompt of the set of AI prompts is a text string.
This reads on a human (e.g., mentally and/or using pen and paper):
Working with prompts/sentences that are in a predefined format (text).
No additional limitations are present.
With respect to claim 5, the claim(s) recite:
5. The computer-implemented method of claim 1 wherein the computer-readable text is plaintext.
This reads on a human (e.g., mentally and/or using pen and paper):
Working with prompts/sentences that are in a predefined format (plaintext).
No additional limitations are present.
With respect to claim 6, the claim(s) recite:
6. The computer-implemented method of claim 1 wherein the set of analyses from the AI engine is received as a JavaScript Object Notation (JSON) object.
This reads on a human (e.g., mentally and/or using pen and paper):
Working with prompts/sentences that are in a predefined format (JSON).
No additional limitations are present.
With respect to claim 7, the claim(s) recite:
7. The computer-implemented method of claim 1 wherein each analysis of the set of analyses is a text string.
This reads on a human (e.g., mentally and/or using pen and paper):
Working with analysis that are in a predefined format (text).
No additional limitations are present.
With respect to claim 8, the claim(s) recite:
8. The computer-implemented method of claim 1 wherein each analysis of a subset of the set of analyses is a text string encoding a Boolean value.
This reads on a human (e.g., mentally and/or using pen and paper):
Working with analysis that are in a predefined format (text/Boolean value).
No additional limitations are present.
With respect to claims 9 and 16, the claim(s) recite:
9 and 16. The computer-implemented method/computer system of claims 1 and 15 further comprising:
transforming the set of analyses to create a transformed set of analyses,
wherein the feature of merit is based on the transformed set of analyses.
This reads on a human (e.g., mentally and/or using pen and paper):
Rewriting the analysis using a predetermined set of rules;
Wherein the assigned value is based on the rewritten analysis.
No additional limitations are present.
With respect to claim 10, the claim(s) recite:
10. The computer-implemented method of claim 9 wherein the transforming includes converting data from a first data type to a second data type.
This reads on a human (e.g., mentally and/or using pen and paper):
Wherein the rewriting the analysis using a predetermined set of rules comprises converting a first data to second data.
No additional limitations are present.
With respect to claim 11, the claim(s) recite:
11. The computer-implemented method of claim 10 wherein the first data type is a string and the second data type is a Boolean.
This reads on a human (e.g., mentally and/or using pen and paper):
Wherein the first data is text and the second data is Boolean values.
No additional limitations are present.
With respect to claims 12 and 17, the claim(s) recite:
12 and 17. The computer-implemented method/computer system of claims 1 and 15 wherein the feature of merit is a summed score and, for the interaction, each analysis of the set of analyses contributes either zero or one to the summed score.
This reads on a human (e.g., mentally and/or using pen and paper):
Defining the assigning of values to the interactions from the speech/audio (e.g., sum of scores, contribution either 0 or 1).
No additional limitations are present.
With respect to claims 13 and 18, the claim(s) recite:
13 and 18. The computer-implemented method/computer system of claims 12 and 18 wherein the feature of merit is automatically set to a failing score in response to any one or more of a defined subset of the set of analyses failing to meet satisfaction criteria.
This reads on a human (e.g., mentally and/or using pen and paper):
Further defining the assigning of values to the interactions from the speech/audio (e.g., considering thresholds/satisfaction criteria).
No additional limitations are present.
With respect to claims 14 and 19, the claim(s) recite:
14 and 19. The computer-implemented method/computer system of claims 1 and 15 wherein elements of the set of analyses correspond one-to-one with elements of the set of AI prompts.
This reads on a human (e.g., mentally and/or using pen and paper):
Wherein the analysis includes elements predefined in the prompts/sentences.
No additional limitations are present.
Claim Rejections - 35 USC § 103
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.
The factual inquiries for establishing a background for determining obviousness under 35 U.S.C. 103 are summarized as follows:
1. Determining the scope and contents of the prior art.
2. Ascertaining the differences between the prior art and the claims at issue.
3. Resolving the level of ordinary skill in the pertinent art.
4. Considering objective evidence present in the application indicating obviousness or nonobviousness.
This application currently names joint inventors. In considering patentability of the claims the examiner presumes that the subject matter of the various claims was commonly owned as of the effective filing date of the claimed invention(s) absent any evidence to the contrary. Applicant is advised of the obligation under 37 CFR 1.56 to point out the inventor and effective filing dates of each claim that was not commonly owned as of the effective filing date of the later invention in order for the examiner to consider the applicability of 35 U.S.C. 102(b)(2)(C) for any potential 35 U.S.C. 102(a)(2) prior art against the later invention.
Claims 1, 3-5, 7, 9, 14-16, and 19-20 is/are rejected under 35 U.S.C. 103 as being unpatentable over Trzyna (US 20240338860 A1) and further in view of Miller (US 11057519 B1) and Callegari et al. (US 20240362422 A1).
As to independent claim 1, Trzyna teaches:
1. A computer-implemented method for processing an audio recording of an interaction (see ¶ [0003]: “Examples described in this disclosure relate to systems and methods for providing live image generation based on audio transcription. In an example implementation, image generation systems and methods are described that convert a live audio stream, such as a conversation, speech, lecture, etc., into a live text transcript using speech-to-text conversion. ...”), the computer-implemented method comprising:
obtaining the audio recording (see ¶ [0014]: “… According to an example implementation, the input devices include one or more microphones 114 for receiving audible input. In some examples, the audible input includes a live audio stream of spoken, sung, or otherwise uttered words and/or phrases...”);
automatically transcribing the audio recording into computer-readable text (see ¶ [0017]: “In an example implementation, the image generator 110 converts the live audio stream into a live text transcript that is created as the words are being spoken. For instance, the image generator 110 performs speech-to-text to generate the live text transcript in real time or near-real time…”),
determining a set of artificial intelligence (AI) prompts corresponding to the interaction (see ¶ [0017]: “… In an example implementation, a segment of the live text transcript is extracted, and the image generator 110 generates a first prompt to query a first language model (LM) for a summary of a segment of the live text transcript…”),
wherein each prompt of the set of Al prompts corresponds to a respective compliance rule applicable to the interaction (see ¶ [0017-0018, 0022, 0028-0029, and 0032]: “[0017] In an example implementation, the image generator 110 converts the live audio stream into a live text transcript that is created as the words are being spoken. For instance, the image generator 110 performs speech-to-text to generate the live text transcript in real time or near-real time. In an example implementation, a segment of the live text transcript is extracted, and the image generator 110 generates a first prompt to query a first language model (LM) for a summary of a segment of the live text transcript. In some examples, the first LM prompt includes summarization instructions and the live text transcript segment. According to examples, the image generator 110 provides the first LM prompt to the first LM and receives, in response to the first LM prompt, the summarization of the live text transcript segment. In some examples, the first LM is a large language model (LLM) 108 trained to understand and generate sequences of tokens, which may be in the form of natural language (e.g., human-like text). In various examples, the LLM 108 can understand complex intent, cause and effect, perform language translation, semantic search classification, complex classification, text sentiment, summarization, summarization for an audience, and/or other natural language capabilities. [0018] The image generator 110 further generates a second prompt to query a second LM for generating an image related to the LM generated summarization, referred to herein as a second LM prompt or an image-request LM prompt. In some examples, the second LM prompt includes image-generation priming instructions and the summarization generated by the LLM 108. In an example implementation, the second LM is a text-to-image model, herein referred to as an image-generation AI model 118. For example, the image-generation AI model 118 may be an LM based on a transformer architecture that is trained to generate images based on textual descriptions, such as the DALL-E model from OpenAI. According to an example, the image-generation AI model 118 uses a combination of natural language processing and computer vision to generate images from textual descriptions. For instance, the image-generation AI model 118 is trained on a large dataset of image-caption pairs and can generate a wide range of images based on textual input. Images, for example can include objects, scenes, anthropomorphic creatures, etc. [0022] …In such examples, the multimodal prompt may be: “Generate an image based on the summary of {SEGMENT},” where {SEGMENT} is replaced by the segment of the text transcript or the segment of the audio file. The multimodal generative AI then generates an image in response to the multimodal prompt. [0028] In some implementations, the prompt generator 204 generates summarization instructions for the first LM prompt 215 corresponding to summarizing the segment of the live text transcript 210. For instance, the summarization instructions may include directives to the LLM 108, such as “summarize the following:”. In some examples, the summarization instructions include text length instructions (e.g., “limit the summarization to N words or less”, where N is a predetermined number)… [0029] …For instance, such information can be used to apply further guardrail instructions in a second prompt 235 (e.g., to request an image appropriate for a child audience). In other examples, the summarization instructions include additional and/or alternative instructions. [0032] In some examples, the image-generation priming instructions for the second prompt include guardrail instructions corresponding to allowing or disallowing certain image content or types of image content. For instance, image-generation priming instructions including guardrail instructions may include language such as, “generate an image appropriate for all audiences of:”, “generate a painting without nudity or sexual content of:”, “create a cartoon without violence:”, etc., followed by the LM-generated summarization 230…”);
providing the computer-readable text and the set of AI prompts to an AI engine (see ¶ [0017] citations as in limitations above and further Fig. 1 and 4,
¶ [0018]: “The image generator 110 further generates a second prompt to query a second LM for generating an image related to the LM generated summarization, referred to herein as a second LM prompt or an image-request LM prompt. In some examples, the second LM prompt includes image-generation priming instructions and the summarization generated by the LLM 108. In an example implementation, the second LM is a text-to-image model, herein referred to as an image-generation AI model 118. For example, the image-generation AI model 118 may be an LM based on a transformer architecture that is trained to generate images based on textual descriptions, such as the DALL-E model from OpenAI...”
and ¶ [0022]: “The image-generation AI model 118 and the LLM 108 are generally both considered to be generative AI models. In some examples, the image-generation AI model 118 and the LLM 108 may be combined into, or replaced by, a single multi-modal generative AI model that is capable of generating and/or processing multiple forms of inputs and outputs, such as text, images, and or audio. In such examples where a multimodal generative AI model is used, the first LM prompt and the second LM prompt may be effectively combined. For instance, a multi-modal prompt may be generated that includes a set of instructions to generate an image based on a summarization of the segment of the text transcript or even a segment of the audio itself. In such examples, the multimodal prompt may be: “Generate an image based on the summary of {SEGMENT},” where {SEGMENT} is replaced by the segment of the text transcript or the segment of the audio file. The multimodal generative AI then generates an image in response to the multimodal prompt.”);
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However, Trzyna does not explicitly teach, but Miller does teach:
wherein:
the computer-readable text includes a set of speaker tags and a set of time tags (see ¶ Col. 7, lines 50-62: “(37) Ingestion server 116 comprises a combination of hardware and software (e.g., ingestion server component 117) operable to process voice session recordings recorded by recording server 114 or live calls and perform speech-to-text transcription to convert live or recorded calls to text transcripts. Ingestion server 116 stores the transcription of a voice session in association with the voice session in data store 118. In some embodiments, the voice session is split between channels (e.g., agent channel, incoming caller channel) such that ingestion server 116 generates a transcript for each channel. Each transcript can include time labels such that text in a transcript can be synchronized with the recording of the voice session during playback.”),
each speaker tag of the set of speaker tags identifies, for a respective portion of the computer-readable text, which entity of the interaction produced the respective portion (see ¶ Col. 7, lines 50-62 citation as in limitation above, more specifically: “… the voice session is split between channels (e.g., agent channel, incoming caller channel) such that ingestion server 116 generates a transcript for each channel…”), and
the set of time tags is configured to synchronize the computer- readable text to the audio recording (see ¶ Col. 7, lines 50-62 citation as in limitation above, more specifically: “… Each transcript can include time labels such that text in a transcript can be synchronized with the recording of the voice session during playback.” and ¶ Col. 8, lines 40-67: “(42) Recording server 114 stores data and voice sessions in data store 118, which may comprise one or more databases, file systems or other data stores, including distributed data stores. Recording server 114 stores a voice session recording as a transaction in data store 118. A transaction may comprise transaction metadata and associated session data. For example, when recording server 114 records a voice session, recording server 114 can associate the recording with a unique transaction id and store a transaction having the transaction id in data store 118. A data session may also be linked to the transaction id. Thus, the transaction may further include a recording of a data session associated with the call, such as a series of screen shots captured from the agent computer 164 during a voice session. The transaction may also include a transcript of the voice session recording created by ingestion server 116. In some embodiments, the voice session may be recorded as separate recordings of the agent and caller and thus, a transaction may include an agent recording, a customer recording, a transcript of the recording of the agent (agent transcript) and a transcript of the recording of the customer (inbound caller transcript). Each transcript can include time labels such that text in a transcript can be synchronized with the recording of the voice session during playback. According to one embodiment, the voice session recording, transcript of the voice session or data session recording for a call may be stored in a file system and the transaction metadata stored in a database with pointers to the associated files for the transaction.”);
Trzyna and Miller are considered to be analogous to the claimed invention because they are in the same field of endeavor in processing natural language. Therefore, it would have been obvious to someone of ordinary skill in the art before the effective filing date of the claimed invention to have modified Trzyna to incorporate the teachings of Miller of wherein: the computer-readable text includes a set of speaker tags and a set of time tags, each speaker tag of the set of speaker tags identifies, for a respective portion of the computer-readable text, which entity of the interaction produced the respective portion, and the set of time tags is configured to synchronize the computer- readable text to the audio recording which provides the benefit of automatically and accurately evaluating a large number of transactions as well as more accurately tuning auto answering (¶ Col. 4, lines 19-27 of Miller).
However, Trzyna in combination with Miller do not explicitly teach, but Callegari et al. does teach:
receiving a set of analyses from the AI engine corresponding to the set of AI prompts (see ¶ [0004]: “A computing system for revising large language model (LLM) input prompts is provided herein. In one example, the computing system includes at least one processor configured to cause a prompt interface for a trained LLM to be presented, and receive, via the prompt interface, a prompt from a user including an instruction for the LLM to generate an output. In this example, the at least one processor is configured to provide first input including the prompt to the LLM, and generate, in response to the first input, a first response to the prompt via the LLM. The at least one processor is configured to perform assessment and revision of the prompt, at least in part by assessing the first response according to assessment criteria to generate an assessment report for the first response, via the LLM, providing second input including the first prompt, the first response, the assessment report, and a prompt revision instruction to revise the prompt in view of the assessment report to the LLM, and generating a revised prompt in response to the second input, via the LLM. The at least one processor is configured to provide final input including the revised prompt to the LLM; in response to the final input, generate a final response to the revised prompt, via the LLM; and output the final response to the user.”);
determining a feature of merit for the interaction based on the set of analyses (see ¶ [0004] citation as in limitation above and further ¶ [0033]: “…The assessment report 34 may include numeric scores computed for each of the assessment criteria 64 on a scale from 1-10, as well as a natural language (textual) description of the reasons for the score for each of the assessment criteria 64, for example…”); and
in response to the feature of merit, selectively generating a notification and transmitting the notification using a selected communications channel (see ¶ [0033 and 0044-0051]: “[0033] …Next, the assessment report 34 is displayed to the user, including assessment report text 174, along with a gating control, which asks the user if the user would like to generate a revised response. The assessment report 34 may include numeric scores computed for each of the assessment criteria 64 on a scale from 1-10, as well as a natural language (textual) description of the reasons for the score for each of the assessment criteria 64, for example. In some cases, the at least one processor 16 can be further configured to request and receive information to further specify the prompt 30 from the user. This information may be requested based on the assessment report 34 and/or may be used to generate the assessment criteria 64 used in a future response assessment stage. For example, if an assessment report 34 includes a low assessment of a response 32 to an assessment criterion 64 of “acceptable for intended audience” the system can request further information from the user on the intended audience of the response 32. In addition, if the user specifies the intended audience to be college math professors, or some such similar audience, the assessment criteria can be modified to include “acceptable for audience of college math professors,” etc. The user may be able to compose freeform input, or select from preset answers, as shown in the example of the assessment criteria text input pane 168.
[0044] As provided in this example, the assessment instruction 112 may include a mixture of plain language and markup. In this example, five user assessment criteria 64 are specified by the user. In response, the LLM 26 may output the following first assessment report 34, which may include one or both of a score and a written description of how well the first response 32 met the assessment criteria 64.
[0045] AUDIENCE: The intended audience seems to be 5th grade elementary school students, as specified in the prompt.
[0046] APPROPRIATENESS: 8—The response does a good job of breaking down the article into terms that elementary school students would understand, but it could be even more simplified.
[0047] READABILITY: 8—The response is generally easy to understand, but some of the vocabulary (like “extinct”) may be difficult for some 5th graders.
[0048] SUCCINCTNESS: 9—The response does a good job of summarizing the key points of the article without getting bogged down in details.
[0049] INCLUSIVITY: 10—The response uses language that is accessible for all readers.
[0050] INTERESTING: 7—The response does a good job of summarizing the article, but it could have used more exciting language to capture the attention of 5th graders.
[0051] The PREVIOUS_PROMPT and assessment report 34 are then fed back into the LLM 26 with further instructions: “Create an improved PROMPT that will yield a better result, based on these ratings.” The improved prompt 69 outputted by the LLM 26 may be “Summarize the above article for a 5th grade elementary school student, using simple vocabulary and exciting language to make it engaging for young readers.” This ends the first iteration 50 of refinement.”).
Trzyna, Miller, and Callegari et al. are considered to be analogous to the claimed invention because they are in the same field of endeavor in processing natural language. Therefore, it would have been obvious to someone of ordinary skill in the art before the effective filing date of the claimed invention to have modified Trzyna in combination with Miller to incorporate the teachings of Callegari et al. of receiving a set of analyses from the AI engine corresponding to the set of AI prompts; determining a feature of merit for the interaction based on the set of analyses; and in response to the feature of merit, selectively generating a notification and transmitting the notification using a selected communications channel which provides the benefit of resulting in both an improved prompt and an improved final response ([0035] of Callegari et al.).
As to independent claim 15, Trzyna and Callegari et al. teach the limitations as in claim 1, above.
Trzyna further teaches:
15. A computer system (see ¶ [0003]: “Examples described in this disclosure relate to systems and methods for providing live image generation based on audio transcription. In an example implementation, image generation systems and methods are described that convert a live audio stream, such as a conversation, speech, lecture, etc., into a live text transcript using speech-to-text conversion. ...”) comprising:
memory hardware configured to store instructions (see ¶ [0064]: “The term computer readable media as used herein 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, or program modules. The system memory 504, the removable storage device 509, and the non-removable storage device 510 are all computer readable media examples (e.g., memory storage.) Computer readable media include random access memory (RAM), read-only memory (ROM), electrically erasable programmable 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 article of manufacture which can be used to store information and which can be accessed by the computing device 500….”), and
processing hardware configured to execute the instructions (see ¶ [0062]: “Furthermore, examples of the disclosure may be practiced in an electrical circuit comprising discrete electronic elements, packaged or integrated electronic chips containing logic gates, a circuit utilizing a microprocessor, or on a single chip containing electronic elements or microprocessors. For example, examples of the disclosure may be practiced via a system-on-a-chip (SOC) where each or many of the components illustrated in FIG. 5 may be integrated onto a single integrated circuit...”), wherein the instructions include:
[the limitations as in claim 1, above].
As to independent claim 20, Trzyna, Miller, and Callegari et al. teach the limitations as in claim 1, above.
Trzyna further teaches:
20. A non-transitory computer-readable medium comprising processor-executable instructions (see ¶ [0064]: “The term computer readable media as used herein 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, or program modules…”) that include:
[the limitations as in claim 1, above].
Regarding claim 3, Trzyna, Miller, and Callegari et al. teach the limitations as in claim 1, above.
Trzyna further teaches:
3. The computer-implemented method of claim 1 wherein the set of AI prompts is universal to all interactions (see ¶ [0017]: “In an example implementation, the image generator 110 converts the live audio stream into a live text transcript that is created as the words are being spoken. For instance, the image generator 110 performs speech-to-text to generate the live text transcript in real time or near-real time…” and further ¶ [0017]: “… In an example implementation, a segment of the live text transcript is extracted, and the image generator 110 generates a first prompt to query a first language model (LM) for a summary of a segment of the live text transcript…”
Here, the Examiner notes that the broadness of the prompts being universal to all interactions is read by Trzyna with the generic audio transcription (i.e., speech-to-text) format.).
Regarding claim 4, Trzyna, Miller, and Callegari et al. teach the limitations as in claim 1, above.
Trzyna further teaches:
4. The computer-implemented method of claim 1 wherein each prompt of the set of AI prompts is a text string (see ¶ [0017] citations as in claims 1 and 3, above.: “speech-to-text”).
Regarding claim 5, Trzyna, Miller, and Callegari et al. teach the limitations as in claim 1, above.
Trzyna further teaches:
5. The computer-implemented method of claim 1 wherein the computer-readable text is plaintext (see ¶ [0017] citations as in claims 1 and 3, above.: “speech-to-text”).
Regarding claim 7, Trzyna, Miller, and Callegari et al. teach the limitations as in claim 1, above.
Trzyna further teaches:
7. The computer-implemented method of claim 1 wherein each analysis of the set of analyses is a text string (see ¶ [0017] citations as in claims 1 and 3, above and further ¶ [0018]: “The image generator 110 further generates a second prompt to query a second LM for generating an image related to the LM generated summarization, referred to herein as a second LM prompt or an image-request LM prompt. In some examples, the second LM prompt includes image-generation priming instructions and the summarization generated by the LLM 108. In an example implementation, the second LM is a text-to-image model, herein referred to as an image-generation AI model 118. For example, the image-generation AI model 118 may be an LM based on a transformer architecture that is trained to generate images based on textual descriptions, such as the DALL-E model from OpenAI...”
and ¶ [0022]: “The image-generation AI model 118 and the LLM 108 are generally both considered to be generative AI models. In some examples, the image-generation AI model 118 and the LLM 108 may be combined into, or replaced by, a single multi-modal generative AI model that is capable of generating and/or processing multiple forms of inputs and outputs, such as text, images, and or audio. In such examples where a multimodal generative AI model is used, the first LM prompt and the second LM prompt may be effectively combined. For instance, a multi-modal prompt may be generated that includes a set of instructions to generate an image based on a summarization of the segment of the text transcript or even a segment of the audio itself. In such examples, the multimodal prompt may be: “Generate an image based on the summary of {SEGMENT},” where {SEGMENT} is replaced by the segment of the text transcript or the segment of the audio file. The multimodal generative AI then generates an image in response to the multimodal prompt.”).
Regarding claims 9 and 16, Trzyna, Miller, and Callegari et al. teach the limitations as in claim 1 and 15, above.
Callegari et al. further teaches:
9 and 16. The computer-implemented method/computer system of claims 1 and 15 further comprising:
transforming the set of analyses to create a transformed set of analyses (see ¶ [0051-0064]: “[0051] The PREVIOUS_PROMPT and assessment report 34 are then fed back into the LLM 26 with further instructions: “Create an improved PROMPT that will yield a better result, based on these ratings.” The improved prompt 69 outputted by the LLM 26 may be “Summarize the above article for a 5th grade elementary school student, using simple vocabulary and exciting language to make it engaging for young readers.” This ends the first iteration 50 of refinement.
[0052] For the second iteration 52, the improved prompt 69 and original article 148 are inputted to the LLM 26. This time, the generation 2 response 72 may be “The article above is all about the giant panda, a bear that lives in China. The giant panda is known for its black-and-white fur and love of eating bamboo. Unfortunately, because of people destroying its habitat, the giant panda is now a vulnerable species. But the good news is that there are people working hard to help the panda, and the population is starting to grow again. The giant panda is so important to China that it's often used as a symbol for the country.” The same self-assessment instructions 112 as in the first iteration may be submitted, and the result may be as follows.
[0053] APPROPRIATENESS: 8—The response is appropriate for the intended audience, using simple vocabulary and providing a basic summary of the article. However, some of the more scientific terms (like “vulnerable species”) may need further explanation for a 5th grader.
[0054] READABILITY: 9—The response is very readable, with short sentences and simple vocabulary.
[0055] SUCCINCTNESS: 8—The response does a good job of summarizing the article in a short paragraph, although it could be even more succinct if it left out some of the less important details.
[0056] INCLUSIVITY: 10—The response does not use any language or terminology that would exclude anyone from understanding it.
[0057] INTERESTING: 8—The response is engaging and contains some interesting facts about the giant panda, but could be even more exciting if it included more sensory details or a stronger sense of urgency about the panda's plight.
[0058] After being instructed to further revise the revised prompt 69, the LLM 26 may output, “Summarize the above article for a 5th grade elementary school student, using simple vocabulary and exciting language to make it engaging for young readers. Include sensory details to make the summary more interesting, and explain any scientific terms in a way that a 5th grader would understand. Keep the summary short and concise, focusing only on the most important information.” This ends the second iteration…”),
wherein the feature of merit is based on the transformed set of analyses (see ¶ [0051-0064] citations as in limitation above.
Here, the Examiner notes that the transformation of analysis and feature of merit based on said transformation is read by the iterations / edits to the analysis/assessment results changing along with these iterations/edits.).
Trzyna, Miller, and Callegari et al. are considered to be analogous to the claimed invention because they are in the same field of endeavor in processing natural language. Therefore, it would have been obvious to someone of ordinary skill in the art before the effective filing date of the claimed invention to have modified Trzyna to incorporate the teachings of Callegari et al. of transforming the set of analyses to create a transformed set of analyses, wherein the feature of merit is based on the transformed set of analyses which provides the benefit of resulting in both an improved prompt and an improved final response ([0035] of Callegari et al.).
Regarding claims 14 and 19, Trzyna, Miller, and Callegari et al. teach the limitations as in claim 1 and 15, above.
Callegari et al. further teaches:
14 and 19. The computer-implemented method/computer system of claims 1 and 15 wherein elements of the set of analyses correspond one-to-one with elements of the set of AI prompts (see ¶ [0044 and 0051-0064] citations as in claims 9 and 16, above. More specifically: “[0044] As provided in this example, the assessment instruction 112 may include a mixture of plain language and markup. In this example, five user assessment criteria 64 are specified by the user. In response, the LLM 26 may output the following first assessment report 34, which may include one or both of a score and a written description of how well the first response 32 met the assessment criteria 64. [0045] AUDIENCE: The intended audience seems to be 5th grade elementary school students, as specified in the prompt. [0046] APPROPRIATENESS: 8—The response does a good job of breaking down the article into terms that elementary school students would understand, but it could be even more simplified. [0047] READABILITY: 8—The response is generally easy to understand, but some of the vocabulary (like “extinct”) may be difficult for some 5th graders. [0048] SUCCINCTNESS: 9—The response does a good job of summarizing the key points of the article without getting bogged down in details. [0049] INCLUSIVITY: 10—The response uses language that is accessible for all readers. [0050] INTERESTING: 7—The response does a good job of summarizing the article, but it could have used more exciting language to capture the attention of 5th graders.”).
Claim 2 is/are rejected under 35 U.S.C. 103 as being unpatentable over Trzyna (US 20240338860 A1), Miller (US 11057519 B1) and Callegari et al. (US 20240362422 A1) as applied to claim 1, above and further in view of Mukherji et al. (US 20200278999 A1).
Regarding claim 2, Trzyna, Miller, and Callegari et al. teaches the limitations as in claim 1, above.
However, Trzyna, Miller, and Callegari et al. do not explicitly teach, but Mukherji et al. does teach:
2. The computer-implemented method of claim 1 wherein the audio recording includes a compressed audio file (see ¶ [0086]: “For example, the multi-modal transformation engine 131 may receive, as primary provider input, a compressed audio file, decompress the compressed audio file to generate a decompressed audio file, detect (e.g., using a machine learning module) background noise in the decompressed audio file, remove the detected background noise (e.g., utilizing a filter module, operating as a high-pass filter, a low-pass filter, a band-pass filter, and/or the like...”).
Trzyna, Miller, and Callegari et al. and Mukherji et al. are considered to be analogous to the claimed invention because they are in the same field of endeavor in data processing (i.e., audio/speech). Therefore, it would have been obvious to someone of ordinary skill in the art before the effective filing date of the claimed invention to have modified , Trzyna, Miller, and Callegari et al. to incorporate the teachings of Mukherji et al. of wherein the audio recording includes a compressed audio file which provides the benefit of removing segments and/or portions determined to include background noise ([0083] of Mukherji et al.).
Claim 6 is/are rejected under 35 U.S.C. 103 as being unpatentable over Trzyna (US 20240338860 A1), Miller (US 11057519 B1) and Callegari et al. (US 20240362422 A1) as applied to claim 1, above and further in view of Revankar et al. (US 20220147698 A1).
Regarding claim 6, Trzyna, Miller, and Callegari et al. teaches the limitations as in claim 1, above.
However, Trzyna, Miller, and Callegari et al. do not explicitly teach, but Revankar et al. does teach:
6. The computer-implemented method of claim 1 wherein the set of analyses from the AI engine is received as a JavaScript Object Notation (JSON) object (see ¶ [0023]: “…Based on the dynamic mapping, the AI engine 104 may generate a first executable file such that an execution of the first executable file facilitates generation of the form. For example, the first executable file may be in JavaScript Object Notation (JSON) format…”).
Trzyna, Miller, and Callegari et al. and Revankar et al. are considered to be analogous to the claimed invention because they are in the same field of endeavor in data processing (i.e., text or audio). Therefore, it would have been obvious to someone of ordinary skill in the art before the effective filing date of the claimed invention to have modified Trzyna, Miller, and Callegari et al. to incorporate the teachings of Revankar et al. of wherein the set of analyses from the AI engine is received as a JavaScript Object Notation (JSON) object which provides the benefit of facilitating the generation of forms ([0023] of Revankar et al.).
Claims 8 and 10-11 is/are rejected under 35 U.S.C. 103 as being unpatentable over Trzyna (US 20240338860 A1), Miller (US 11057519 B1) and Callegari et al. (US 20240362422 A1) as applied to claim 1 and/or 9, above and further in view of Shevrin et al. (US 12309180 B1).
Regarding claim 8, Trzyna, Miller, and Callegari et al. teach the limitations as in claim 1, above.
However, Trzyna, Miller, and Callegari et al. do not explicitly teach, but Shevrin et al. does teach:
8. The computer-implemented method of claim 1 wherein each analysis of a subset of the set of analyses is a text string encoding a Boolean value (see ¶ Col. 6, lines 1-38: “(20) The model checking system 150 includes a server 155 and a formal compliance document 151. The server 155 or other suitable device is used to perform the computing functions of the model checking system 150. In an example, the server 155 stores received data from the computer network system 120 and the cloud provider system 140, stores rules and policies, creates models, converts string functions of model data to Boolean functions, performs model checks, analyzes IAM functions, performs determinations related to user access, or performs any other suitable actions. Any other computing or storage function required by the model checking system 150 may be performed by the server 155. The server 155 may represent any number of servers, cloud computing devices, or other types of devices for performing the tasks described herein. In certain examples, some or all of the functions of the model checking system 150 and the server 155 are performed by the computer network system 120, the cloud provider system 140, or any other suitable computing device or service…
¶ Col. 7, lines 50-64: “(27) In certain examples, the system translates non-Boolean variables, such as string variables, into Boolean variables in order to apply an SAT model checker. By encoding the non-Boolean variables into Boolean variables, the system is able to reduce the amount of processing power, time, bandwidth, and required processing capacity of the server or other device operating the model checker. An SMT based model checker attempting to perform model checking on non-Boolean variables requires significantly more processing time and capacity than a similar device performing an SAT based model checker. The Boolean model further allows application of further formal reasoning verification techniques that SMT solvers are not designed to apply, thus creating further inefficiencies in the operation of the computing devices.”
and ¶ Col. 10, lines 42-58: “(46) In block 340, the model checking system 150 converts string variables in the model to Boolean variables. Certain model checkers use satisfiability modulo theories (“SMT”) solvers.”).
Trzyna, Miller, and Callegari et al. and Shevrin et al. are considered to be analogous to the claimed invention because they are in the same field of endeavor in data processing (i.e., text or audio). Therefore, it would have been obvious to someone of ordinary skill in the art before the effective filing date of the claimed invention to have modified Trzyna, Miller, and Callegari et al. to incorporate the teachings of Shevrin et al. of wherein each analysis of a subset of the set of analyses is a text string encoding a Boolean value which provides the benefit of the system being able to reduce the amount of processing power, time, bandwidth, and required processing capacity of the server or other device operating the model checker (¶ Col. 7, lines 50-64 of Shevrin et al.).
Regarding claim 10, Trzyna, Miller, and Callegari et al. teach the limitations as in claim 9, above.
However, Trzyna, Miller, and Callegari et al. do not explicitly teach, but Shevrin et al. does teach:
10. The computer-implemented method of claim 9 wherein the transforming includes converting data from a first data type to a second data type (see ¶ Col. 6, lines 1-38, ¶ Col. 7, lines 50-64, and ¶ Col. 10, lines 42-58 citations as in claim 8, above. i.e., string to Boolean).
Trzyna, Miller, and Callegari et al. and Shevrin et al. are considered to be analogous to the claimed invention because they are in the same field of endeavor in data processing (i.e., text or audio). Therefore, it would have been obvious to someone of ordinary skill in the art before the effective filing date of the claimed invention to have modified Trzyna, Miller, and Callegari et al. to incorporate the teachings of Shevrin et al. of wherein the transforming includes converting data from a first data type to a second data type which provides the benefit of the system being able to reduce the amount of processing power, time, bandwidth, and required processing capacity of the server or other device operating the model checker (¶ Col. 7, lines 50-64 of Shevrin et al.).
Regarding claim 11, Trzyna, Callegari et al., and Shevrin et al. teach the limitations as in claim 10, above.
Shevrin et al. further teaches:
11. The computer-implemented method of claim 10 wherein the first data type is a string and the second data type is a Boolean (see ¶ Col. 6, lines 1-38, ¶ Col. 7, lines 50-64, and ¶ Col. 10, lines 42-58 citations as in claim 8, above. i.e., string to Boolean).
Trzyna, Miller, and Callegari et al. and Shevrin et al. are considered to be analogous to the claimed invention because they are in the same field of endeavor in data processing (i.e., text or audio). Therefore, it would have been obvious to someone of ordinary skill in the art before the effective filing date of the claimed invention to have modified Trzyna, Miller, and Callegari et al. to incorporate the teachings of Shevrin et al. of wherein the first data type is a string and the second data type is a Boolean which provides the benefit of the system being able to reduce the amount of processing power, time, bandwidth, and required processing capacity of the server or other device operating the model checker (¶ Col. 7, lines 50-64 of Shevrin et al.).
Claims 12-13 and 17-18 is/are rejected under 35 U.S.C. 103 as being unpatentable over Trzyna (US 20240338860 A1), Miller (US 11057519 B1) and Callegari et al. (US 20240362422 A1) as applied to claims 1 and 15, above and further in view of Dewhare et al. (US 20230244552 A1).
Regarding claims 12 and 17, Trzyna, Miller, and Callegari et al. teach the limitations as in claims 1 and 15, above.
However, Trzyna, Miller, and Callegari et al. do not explicitly teach, but Dewhare et al. does teach:
12 and 17. The computer-implemented method/computer system of claims 1 and 15 wherein the feature of merit is a summed score and, for the interaction, each analysis of the set of analyses contributes either zero or one to the summed score (see ¶ [0106 and 0113]: “[0106] In some embodiments, multiple different comparisons may be performed. The percentage match for each comparison may be summed, and then normalized to a value between 0 and 1 to finally determine the confidence score for each match. Matches below a minimum confidence score threshold may be discarded, such that matches are not created between database entities and metadata attributes that are highly unlikely to be related.
[0113] In some embodiments, the user interface engine 320 may present the database entity for the highest confidence match as a recommended database entity if the confidence score for the highest confidence match is greater than a predetermined threshold. If the confidence score for the highest confidence match fails to meet the predetermined threshold, then the user interface engine 320 may provide a set of database entities that are potentially desirable for selection by the user.”).
Trzyna, Miller, and Callegari et al. and Dewhare et al. are considered to be analogous to the claimed invention because they are in the same field of endeavor in data processing (i.e., text or audio). Therefore, it would have been obvious to someone of ordinary skill in the art before the effective filing date of the claimed invention to have modified Trzyna, Miller, and Callegari et al. to incorporate the teachings of Dewhare et al. of wherein the feature of merit is a summed score and, for the interaction, each analysis of the set of analyses contributes either zero or one to the summed score which provides the benefit of discarding attributes that are highly unlikely to be related (¶ [0106] of Dewhare et al.).
Regarding claims 13 and 18, Trzyna, Miller, and Callegari et al. teach the limitations as in claims 1 and 17, above.
However, Trzyna, Miller, and Callegari et al. do not explicitly teach, but Dewhare et al. does teach:
13 and 18. The computer-implemented method/computer system of claims 12 and 17 wherein the feature of merit is automatically set to a failing score in response to any one or more of a defined subset of the set of analyses failing to meet satisfaction criteria (see ¶ [0106 and 0113]: “[0106] In some embodiments, multiple different comparisons may be performed. The percentage match for each comparison may be summed, and then normalized to a value between 0 and 1 to finally determine the confidence score for each match. Matches below a minimum confidence score threshold may be discarded, such that matches are not created between database entities and metadata attributes that are highly unlikely to be related.
[0113] In some embodiments, the user interface engine 320 may present the database entity for the highest confidence match as a recommended database entity if the confidence score for the highest confidence match is greater than a predetermined threshold. If the confidence score for the highest confidence match fails to meet the predetermined threshold, then the user interface engine 320 may provide a set of database entities that are potentially desirable for selection by the user.”).
Trzyna, Miller, and Callegari et al. and Dewhare et al. are considered to be analogous to the claimed invention because they are in the same field of endeavor in data processing (i.e., text or audio). Therefore, it would have been obvious to someone of ordinary skill in the art before the effective filing date of the claimed invention to have modified Trzyna, Miller, and Callegari et al. to incorporate the teachings of Dewhare et al. of wherein the feature of merit is automatically set to a failing score in response to any one or more of a defined subset of the set of analyses failing to meet satisfaction criteria which provides the benefit of discarding attributes that are highly unlikely to be related (¶ [0106] of Dewhare et al.).
Conclusion
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 Keisha Y Castillo-Torres whose telephone number is (571)272-3975. The examiner can normally be reached Monday - Friday, 9:00 am - 4:00 pm (EST).
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If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, Pierre-Louis Desir can be reached at (571)272-7799. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
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Keisha Y. Castillo-Torres
Examiner
Art Unit 2659
/Keisha Y. Castillo-Torres/Examiner, Art Unit 2659
/PIERRE LOUIS DESIR/Supervisory Patent Examiner, Art Unit 2659
Prosecution Insights