NON-FINAL REJECTION, FIRST DETAILED ACTION
Status of Prosecution
The present application, 18/384,232 filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA .
The application, filed in the Office on Oct. 26, 2023, which in turn claims priority to provisional applications 63/419902, 63/419902, 63/419,942 all filed on Oct. 27, 2022.
Claims 61-80 are pending and were considered presented for examination; all are rejected. Claims 61, 68 and 75 are independent. Claims 1-60 and 81-214 are canceled by preliminary amendment.
Status of Claims
Claims 1-60 and 81-214 are canceled by preliminary amendment.
Claims 61-80 are rejected under 35 U.S.C. § 101 because the claimed invention is directed to an abstract idea without significantly more.
Claims 61-62, 68-69 and 75-76 are rejected under 35 U.S.C. § 102 as being anticipated by Kukde et al. (“Kukde”), United States Patent Application Publication 2024/0005085, published on Jan. 4, 2024.
Claims 63, 70 and 77 are rejected under 35 U.S.C. § 103 as being unpatentable over Kukde in view of non-patent literature Zhang et al., (“Zhang”), “Leveraging Pretrained Models for Automatic Summarization of Doctor-Patient Conversations,” published in 2021.
Claims 64, 71 and 78 are rejected under 35 U.S.C. § 103 as being unpatentable over Kukde in view of Attwater et al. (“Attwater”), United States Patent Application Publication 2023/0244855, published on Aug. 3, 2023.
Claims 65 and 72 are rejected under 35 U.S.C. § 103 as being unpatentable over Kukde in view of Lipton et al. (“Lipton”), United States Patent Application Publication 2022/0375605, published on Nov. 24, 2022.
Claims 66-67, 73-74 and 79-80 are rejected under 35 U.S.C. § 103 as being unpatentable over Kukde in view of Sabapathy et al. (“Sabapathy”), United States Patent 12,217,013, published on Feb. 4, 2025.
Claim Rejections – 35 USC § 101 – Subject Matter Eligibility
Claims 61-80 are rejected under 35 U.S.C. § 101 because the claimed invention is directed to an abstract idea without significantly more.
Regarding representative claim 61, at step 1, the claim recites a computer system with a processor, and therefore is a manufacture, which is a statutory category of invention. See MPEP § 2106.03.
At step 2A, prong one, the claim recites a system for automatically generating summary output.
The following limitations are the abstract idea of a mental process that can practically be performed in the human mind, with or without the use of a physical aid such as pen and paper (including an observation, evaluation, judgment, opinion). See MPEP § 2106.04(a)(2)(III).
generate a transcript from an interaction including content, the content including at least one of written text, audio speech, non-word symbols, metadata, silences, language characteristics, or acoustic characteristics, wherein the content is attributed to a participant in the interaction; and
generate an interaction summary of the transcript using at least one of an extractive machine learning summarization model or an abstractive machine learning summarization model that summarizes the content of the interaction.
At step 2A prong 2, the claim language is analyzed to determine whether it recites additional elements that integrate the judicial exception into a practical application. See MPEP § 2106.04(d).
The limitation:
one or more processors; and one or more computer readable hardware storage devices having stored computer-executable instructions that are executable by the one or more processors to cause the interaction summarization system to perform steps.
This that are related to generic recitation of computer parts which are adding insignificant extra-solution activity to the judicial exception. See MPEP § 2106.05(g).
Next, at step 2B of the analysis, the claim is considered if it recites additional elements that amount to significantly more than the judicial exception. See MPEP § 2106.05.
The additional element of the processors and the memory do not integrate the abstract idea into a practical application because they do not impose any meaningful limits on practicing the abstract idea. The claim is therefore directed to an abstract idea.
Therefore, claim 61 is ineligible.
As to dependent claims 62-67, the analysis of the respective parent claim is incorporated. In the step 2A, prong one analysis, the additional limitations deal with how the training is conducted which are mathematical calculations or are mental processes. See MPEP § 2106.04(a)(2).
The claims are also ineligible.
As to independent claims 68 and 75, they are rejected for similar reasons as claim 61. Their dependent claims are rejected similarly to their corresponding dependent claims.
Claim Rejections – 35 USC § 102
The following is a quotation of the appropriate paragraphs of 35 U.S.C. 102 that form the basis for the rejections under this section made in this Office action:
A person shall be entitled to a patent unless –
(a)(1) the claimed invention was patented, described in a printed publication, or in public use, on sale, or otherwise available to the public before the effective filing date of the claimed invention.
Claims 61-62, 68-69 and 75-76 are rejected under 35 U.S.C. § 102 as being anticipated by Kukde et al. (“Kukde”), United States Patent Application Publication 2024/0005085, published on Jan. 4, 2024.
As to Claim 61, Kukde teaches: An interaction summarization system for automatically generating summary output, comprising:
one or more processors; and one or more computer readable hardware storage devices having stored computer-executable instructions that are executable by the one or more processors to cause the interaction summarization system (Kukde: pars. 0084-85) to at least:
generate a transcript from an interaction including content, the content including at least one of written text, audio speech, non-word symbols, metadata, silences, language characteristics, or acoustic characteristics (Kukde: par. 0045, the system is able to generate transcript in relation to real-time speaker summaries), wherein the content is attributed to a participant in the interaction (Kukde: par. 0045, speaker-specific summaries; and
generate an interaction summary of the transcript using at least one of an extractive machine learning summarization model or an abstractive machine learning summarization model that summarizes the content of the interaction (Kukde: par. 0050, the neural network [300] that is able to perform functions in relation to generating abstractive summaries).
As to Claim 62, Kukde teaches the limitations of claim 61.
Kukde further teaches: wherein the abstractive machine learning summarization model is trained based on long form summarization (Kukde: par0065, long-form topics).
As to Claim 68, it is rejected for similar reasons as claim 61.
As to Claim 69, it is rejected for similar reasons as claim 62.
As to Claim 75, it is rejected for similar reasons as claim 61.
As to Claim 76, it is rejected for similar reasons as claim 62.
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.
A.
Claims 63, 70 and 77 are rejected under 35 U.S.C. § 103 as being unpatentable over Kukde et al. (“Kukde”), United States Patent Application Publication 2024/0005085, published on Jan. 4, 2024 in view of non-patent literature Zhang et al., (“Zhang”), “Leveraging Pretrained Models for Automatic Summarization of Doctor-Patient Conversations,” published in 2021.
As to Claim 63, Kukde teaches the limitations of claim 61.
Kukde may not explicitly teach: wherein the abstractive machine learning summarization model is trained based on chunked/bucketed summarization.
Zhang teaches in general concepts related to using pretrained transformer models for automatically summarizing doctor-patient conversations directly from transcripts (Zhang: Abstract). Specifically, Zhang teaches that chunking is used to create chunks of the transcript (Sec. 3,1, chunking). This chunking is part of a multi-stage approach which uses these chunks of summarization and then aggregates them back together for the final summary (Zhang: Sec. 3, methods, “The methods that we propose in this class differ in how they break down the conversation into parts and therefore, the datasets that are used for fine-tuning their first stage model.”)
It would have been obvious to a person having ordinary skill in the art at a time before the effective filing date of the application to have modified Kukde’s disclosures and teachings by utilizing a chunking and bucketing approach as taught and suggested by Zhang. Such a person would have been motivated to do so with a reasonable expectation of success to improve the summarization process efficiently and optimally.
As to Claim 70, it is rejected for similar reasons as claim 63.
As to Claim 77, it is rejected for similar reasons as claim 63.
B.
Claims 64, 71 and 78 are rejected under 35 U.S.C. § 103 as being unpatentable over Kukde et al. (“Kukde”), United States Patent Application Publication 2024/0005085, published on Jan. 4, 2024 in view of Attwater et al. (“Attwater”), United States Patent Application Publication 2023/0244855, published on Aug. 3, 2023.
As to Claim 64, Kukde teaches the limitations of claim 61.
Kukde may not explicitly teaches: wherein the abstractive machine learning summarization model is trained based on an interaction summary label/short sentence.
Attwater teaches in general concepts related to automatically generating a summary note
related to a digitally-recorded interlocutor conversation, such as a chat transcript (Attwater: Abstract). Specifically, Attwater teaches that the short summaries may be generated using short labels for instance (Attwater: par. 0160).
It would have been obvious to a person having ordinary skill in the art at a time before the effective filing date of the application to have modified Kukde’s disclosures and teachings by utilizing short labels as taught and suggested by Attwater. Such a person would have been motivated to do so with a reasonable expectation of success to improve the summarization process efficiently and optimally.
As to Claim 71, it is rejected for similar reasons as claim 64.
As to Claim 78, it is rejected for similar reasons as claim 64.
C.
Claims 65 and 72 are rejected under 35 U.S.C. § 103 as being unpatentable over Kukde et al. (“Kukde”), United States Patent Application Publication 2024/0005085, published on Jan. 4, 2024 in view of Lipton et al. (“Lipton”), United States Patent Application Publication 2022/0375605, published on Nov. 24, 2022.
As to Claim 65, Kukde teaches the limitations of claim 61.
Kukde further teaches: wherein the extractive machine learning summarization model is configured through training to identify at least one word or phrase from the content, the at least one word or phrase corresponding to the summary output of the interaction.
Lipton teaches in general concepts related to automatically generating formatted annotations of doctor-patient conversations based on classification of detecting contents representing one or more portions of a communication (Lipton: Abstract). Specifically, Lipton teaches that keywords (i.e. one word or phrase from the content)may be extracted for the pre-filtering step in the machine learning model training and implementation stages (Lipton: par. 0006, 39, “noteworthy utterances include relevant keywords).
It would have been obvious to a person having ordinary skill in the art at a time before the effective filing date of the application to have modified Kukde’s disclosures and teachings by utilizing the keyword extraction as taught and suggested by Lipton. Such a person would have been motivated to do so with a reasonable expectation of success to improve the summarization process efficiently and optimally.
As to Claim 72, it is rejected for similar reasons as claim 65.
D.
Claims 66-67, 73-74 and 79-80 are rejected under 35 U.S.C. § 103 as being unpatentable over Kukde et al. (“Kukde”), United States Patent Application Publication 2024/0005085, published on Jan. 4, 2024 in view of Sabapathy et al. (“Sabapathy”), United States Patent 12,217,013, published on Feb. 4, 2025.
As to Claim 66, Kukde teaches the limitations of claim 61.
Kukde may not explicitly teach: wherein the extractive machine learning summarization model is trained based on supervised learning for two-class labels.
Sabapathy teaches in general concepts related to generating a summary of a multi-party interaction including receiving an interaction transcript and analyzing the data to identify specific utterances (Sabapth: Abstract). Specifically, labels may be assigned for relevant portions of a transcript as it is compared against the training summary (Sabapathy: col. 19, lines 40 to 45, “In some embodiments, the training data set can be automatically generated by comparing each sentence in a training transcript to each sentence in a corresponding training summary. For example, one or more terms for each sentence in the training transcript can be assigned a label descriptive of whether the terms are relevant in an optimal summary.”).
It would have been obvious to a person having ordinary skill in the art at a time before the effective filing date of the application to have modified Kukde’s disclosures and teachings by utilizing labels for important portions of the transcript for training and optimization of the models as taught and suggested by Sabapathy. Such a person would have been motivated to do so with a reasonable expectation of success to improve the summarization process efficiently and optimally.
As to Claim 67, Kukde and Sabapathy teaches the limitations of claim 66.
Sabapathy further teaches: wherein a first label is for summary content (Sabpathy: col. 19, lines 40 to 45, the label descriptive of whether it is relevant) and a second label is for non-summary content (Examiner asserts that a label as not being relevant may be a design choice to also have been applied to the rest of the content text).
As to Claim 73-74, it is rejected for similar reasons as claim 66-67.
As to Claim 79-80, it is rejected for similar reasons as claim 66-67.
Conclusion
Any inquiry concerning this communication or earlier communications from the examiner should be directed to JAMES T TSAI whose telephone number is (571)270-3916. The examiner can normally be reached M-F 8-5 Eastern.
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/JAMES T TSAI/ Primary Examiner, Art Unit 2147
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