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 .
Claim Rejections - 35 USC § 112
The following is a quotation of 35 U.S.C. 112(b):
(b) CONCLUSION.—The specification shall conclude with one or more claims particularly pointing out and distinctly claiming the subject matter which the inventor or a joint inventor regards as the invention.
The following is a quotation of 35 U.S.C. 112 (pre-AIA ), second paragraph:
The specification shall conclude with one or more claims particularly pointing out and distinctly claiming the subject matter which the applicant regards as his invention.
Claims 4-5, 7, 11-12, 14, 18-20 are rejected under 35 U.S.C. 112(b) or 35 U.S.C. 112 (pre-AIA ), second paragraph, as being indefinite for failing to particularly point out and distinctly claim the subject matter which the inventor or a joint inventor (or for applications subject to pre-AIA 35 U.S.C. 112, the applicant), regards as the invention.
Claim 4 line 3, recites "the duplicate question." It is unclear whether this term is references a currently asked or previously asked duplicate question since these multiple types of duplicate questions precede "the duplicate question" of line 3. For claim interpretation in the interest of compact prosecution, "the duplicate question" will be construed as --the currently asked duplicate question--. Claims 11 and 18 feature similar indefiniteness issues and have likewise been rejected under 35 U.S.C. 112(b).
In Claim 5, line 2, "duplicate questions" lacks a referential modifier/definite article where the term has already appeared in parent claim 1 so it is unclear if this additional instance of the term refers back to the parent claim term or is attempting to introduce a new instance. For claim interpretation, this limitation will be construed as --the duplicate questions--. Further, in claim 5 "the asker identifier" lacks antecedent basis and will be construed as --an asker identifier--. Claims 12 and 19 feature similar indefiniteness issues and have likewise been rejected under 35 U.S.C. 112(b).
In claim 7, "the meeting transcript text" lacks antecedent basis and will be construed as --meeting transcript text--. Claims 14 and 20 feature similar indefiniteness issues and have likewise been rejected under 35 U.S.C. 112(b).
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 1-20 are rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea under the broadest reasonable interpretation (BRI) without significantly more.
Independent Claims 1, 8, and 15 regard a process that, as drafted under its broadest reasonable interpretation, covers performance of the limitations as a mental process, but for the recitation of generic computer components (e.g., storage devices, processor, memory).
In regards to the process of Claims 1, 8, and 15, the claimed functionality could be practiced as a mental process in the following manner:
training, in real-time a model of questions to identify likely duplicate questions (applicant's specification does not include a clear and unmistakable definition for the model since only example embodiments are discussed (Paragraph 0013), thus the ordinary and customary meaning is relied upon (see Flow Chart for claim term interpretation under the BRI in MPEP 2111(V)) which include a human/manually created model such as a data table wherein a user can write down a list of rules based upon examples or mentally train themselves to identify duplicate questions by considering historical patterns and timing information);
identifying a level of duplication between a question and a previously asked question in a meeting transcript (mental judgment rendered by looking at two transcribed questions);
pointing an asker to where in the meeting transcript the question was the previously asked question (manually pointing or highlighting an earlier instance of the question in a meeting transcript using pen and paper);
arranging all duplicate questions in a single point question, wherein each single point question is directed to one similar topic (rewriting the duplicate questions into one revised representative question using pen and paper); and
generating a new meeting transcript including each individual question and each single point question (rewriting the meeting transcript or editing the existing transcript using pen and paper to include the revised question representative of the duplicates).
This judicial exception is not integrated into a practical application. Outside of the identified abstract idea, the claimed invention only includes computer components (e.g., storage devices, processor, memory) that amount to no more than mere instructions to implement an otherwise abstract idea using generic computer components. In this instance, the computer is merely used as a tool to carry out an otherwise abstract idea and not to improve a basic function of the computer as a tool.
The claim does not include additional elements that are sufficient to amount to significantly more than the judicial exception. The above identified additional generic computer components are no more than mere instructions to apply the exception using generic computer components that are well-known, routine, and conventional as is evidenced by Bancorp Services v. Sun Life (Fed. Cir. 2012) and Alice Corp. v. CLS Bank (2014).
Accordingly, claims 1, 8, and 15 are directed towards patent ineligible subject matter under 35 U.S.C. 101.
The remaining dependent claims fail to add patent eligible subject matter to their respective parent claims:
Claims 2, 9, and 16 regard a human manually writing the recited data into the human understandable model.
Claims 3, 10, and 17 regard a human mentally understanding the intent or goal of a question, writing down duplicate questions in a transcript using pen and paper, and speaking to another person to ask them for a new question or gathering the duplicate questions together on paper using a pen.
Claims 4, 11, and 18 regard comparing timing data of the two questions to mentally evaluate whether a differential is acceptable.
Claims 5, 12, and 19 regard a human performing data gathering on pen and paper, writing down information on the people that asked a question, and presenting the rewritten question to a crowd of other people to seek their approval.
Claims 6 and 13 regard a human writing down the recited information as the meeting progresses.
Claims 7, 14, and 20 regard writing down spoken information, mentally processing text for understanding the claimed information based upon knowledge of a natural language, and mentally evaluating the intent/purpose of the question.
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.
Claims 1, 6, 8, and 15 are rejected under 35 U.S.C. 103 as being unpatentable over Braganza, et al. (U.S. PG Publication: 2022/0182425 A1) in view of Salter, et al. (U.S. PG Publication: 2021/0374355 A1).
With respect to Claim 1, Braganza discloses:
training, (deep learning natural language processing (NLP) model that determines whether there is a duplication between two or more questions, Paragraphs 0019, 0039, and 0043-0045; note that in order to be trained, the NLP deep learning model would require some training process);
identifying a level of duplication between a question and a previously asked question in a meeting transcript (level of duplication in a meeting transcript is based upon both the content (e.g., semantic similarity) and timing of the question, Paragraphs 0036, 0039, 0043, and 0045-0047);
pointing an asker to where in the meeting transcript the question was the previously asked question (timestamp and user ID indicating where in the conference duplicate questions were asked presented to an inquiring user (e.g., a presenter), Paragraphs 0019, 0023, 0040, 0047, and 0052);
arranging all duplicate questions in a single point question, wherein each single point question is directed to one similar topic ("combine the two (or more) questions into a single question to avoid duplication," Paragraph 0019; see also Paragraphs 0030 and 0051 (discussing shared key words or "topics")); and
generating a new meeting transcript including each individual question and each single point question (transcript record includes the "two or more questions" combined into a "single one", Paragraphs 0019, 0030, and 0043).
Although Braganza teaches a process similar to the claimed process for duplicate question detection and the inherent training of a “trained” deep NLP model. Braganza fails to mention that the training process is in real time. Salter, however, discloses that a machine learning model trained to perform semantic analysis including the detection of repeated words (Paragraphs 0027 and 0038) may be dynamically trained in "real time" 0029 and 0032).
Braganza and Salter are analogous art because they are from a similar field of endeavor in semantic analysis using machine learning. Thus, it would have been obvious to ordinary skill in the art before the effective filing date to add the real-time aspect of training taught by Salter to the deep machine learning NLP model taught by Braganza to provide a predictable result of allowing the model to continually improve accuracy and reliability over time.
With respect to Claim 6, Braganza further discloses:
The method of claim 1, wherein the new meeting transcript is generated in real-time during the meeting, and includes a timestamp when the question was asked, an asker identifier, each individual question, and each single point question (transcript report is updated on an ongoing based to be current/real-time, Paragraph 0052; includes timestamps and user IDs, Paragraph 0049; and individual and combined/single questions, Paragraphs 0030 and 0053).
Claim 8 relates to an embodiment of the invention in the form of a non-transitory tangible storage device storing program code executable by a computer processor to carry out the method of claim 1, and thus, is rejected under similar rationale. Moreover, Braganza teaches method implementation as a program stored on a non-transitory computer-readable storage device (Paragraph 0054).
Claim 13 recites subject matter similar to Claim 6, and thus, is rejected under similar rationale.
Claim 15 relates to a system embodiment of the invention comprising one or more processors coupled to a memory storing program instructions to carry out the method of claim 1, and thus, is rejected under similar rationale. Moreover, Braganza teaches method implementation as a system comprising a memory storing a program coupled with a computer processor (Paragraph 0054 and see coupling of a CPU 152 to a memory 154 in Fig. 1).
Claims 2, 9, and 16 are rejected under 35 U.S.C. 103 as being unpatentable over Braganza, et al. in view of Salter, et al. and further in view of Harrison (U.S. PG Publication: 2022/0405630 A1).
With respect to Claim 2, Braganza in view of Salter teach the machine learning model that is trained in real-time prior to the process of duplicate question detection in a conference as applied to Claim 1. Braganza in view of Salter do not identify the training data used to train the model set forth in claim 2. Harrison, however, discloses: inputting to the model general teleconferencing content, past meeting transcripts, live chat transcripts, live pre-loaded meeting content, and scripts including specialized language that is customized based on a topic of a meeting (AI model training data input including general teleconference data (e.g., information about the conference), historical transcripts, current audio transcriptions, pre-loaded live content such as documentation, agenda or a result , and agenda items (i.e., topical terms), Paragraphs 0024, 0026, 0029, 0042, and 0045).
Braganza, Salter, and Harrison are analogous art because they are from a similar field of endeavor in semantic analysis using machine learning. Thus, it would have been obvious to ordinary skill in the art before the effective filing date to utilize the AI/machine learning training data taught by Harrison in the deep learning model taught by Braganza in view of Salter to provide a predictable result of enabling the AI/ML model to continue to learn (Harrison, Paragraph 0026) from contextual scenarios.
Claim 9 recites subject matter similar to Claim 2, and thus, is rejected under similar rationale.
Claim 16 recites subject matter similar to Claim 2, and thus, is rejected under similar rationale.
Claims 3-4, 10-11, and 17-18 are rejected under 35 U.S.C. 103 as being unpatentable over Braganza, et al. in view of Salter, et al. and further in view of Tandra, et al. (U.S. PG Publication: 2023/006849 A1).
With respect to Claim 3, Braganza in view of Salter teach the machine learning model that is trained in real-time prior to the process of duplicate question detection in a conference as applied to Claim 1. Braganza further discloses:
based on the analysis, prompting the asker for a new question, or aggregating the duplicate question with other similar duplicate questions ("combine the two (or more) questions into a single question to avoid duplication," Paragraph 0019; see also Paragraphs 0030 and 0051 (discussing shared key words or "topics")), wherein the aggregated duplicate questions are presented for asking (the aggregated duplicate questions into a “single question” are provided for asking, Paragraphs 0019, 0030, and 0053). Braganza in view of Salter does not teach that semantic analysis involves intent determination and the storage of duplicate question in permanent storage for inputting to the model for training.
Tandra, however, discloses that message metrics includes an intent of a message in the form of a question to generate aggregated clusters of similar questions (Paragraphs 0074-0076) where metrics can be stored in a "data store" for future use (Paragraph 0037). Note that the "for inputting" is an intended use recitation and not an active step that patentably limits the claimed method.
Braganza, Salter, and Tandra are analogous art because they are from a similar field of endeavor in semantic analysis using machine learning. Thus, it would have been obvious to ordinary skill in the art before the effective filing date to utilize the intent analysis by Tandra in the semantic duplicate question analysis taught by Braganza in view of Salter to provide an additional similarity metric that can facilitate combining similar messages together (Tandra, Paragraph 0076).
With respect to Claim 4, Braganza further discloses:
The method of claim 1, wherein a low configurable time gap between a currently asked duplicate question and a previously asked duplicate question invokes intent analysis to determine whether the duplicate question is allowed (being in a "same timeframe or window" (i.e., close in time) that can be "predefined" triggers a semantic similarity comparison to determine whether the duplicate is not similar enough/allowed, Paragraphs 0019, 0039-0040, and 0051; wherein semantic similarity includes intent as taught by Tandra as applied to Claim 3).
Claim 10 recites subject matter similar to Claim 3, and thus, is rejected under similar rationale.
Claim 11 recites subject matter similar to Claim 4, and thus, is rejected under similar rationale.
Claim 17 recites subject matter similar to Claim 3, and thus, is rejected under similar rationale.
Claim 18 recites subject matter similar to Claim 4, and thus, is rejected under similar rationale.
Claims 5, 12, and 19 are rejected under 35 U.S.C. 103 as being unpatentable over Braganza, et al. in view of Salter, et al. and further in view of Ternouth (U.S. PG Publication: 2011/0029895 A1).
With respect to Claim 5, Braganza in view of Salter teach the machine learning model that is trained in real-time prior to the process of duplicate question detection in a conference as applied to Claim 1. Braganza further discloses:
aggregating duplicate questions into the single point question based on a degree of duplication, relevancy to a topic, and a gap in timing between the duplicate questions (aggregation into a single/representative question via deep NLP model processing based upon degree of duplication measured by semantic similarity, topical similarity or shared keywords, and a timeframe/window consideration, Paragraphs 0039, 0046-0047, and 0051); and
preserving the asker identifier of each individual question in the single point question (preservation of a “user identifier” for each individual question, Paragraphs 0018, 0040, and 0042).
Although Braganza provides the single replacement question for the duplicate question to a asking/inquiring presenter for review, Braganza in view of Salter does not teach each user is presented with the question for validation as recited in claim 5. Ternouth, however, disclsoes:
presenting the single point question to each asker of each individual question; and iteratively validating and refining with each asker the single point question for accuracy (transmission of a representative question of a group for a question asked a certain number of times for review and iterative voting within the group for approval yielding the intended claimed accuracy result based upon group voting, Paragraphs 0068, 0077, and 0084).
Braganza, Salter, and Ternouth are analogous art because they are from a similar field of endeavor in duplicate message/term processing. Thus, it would have been obvious to ordinary skill in the art before the effective filing date to present the single replacement question for duplicate questions taught by Braganza in view of Salter back to users for voting and approval as taught by Ternouth to provide a predictable result of better ensuring that the single questions sufficiently captures the intention of the group asking the similar/duplicate questions.
Claim 12 recites subject matter similar to Claim 5, and thus, is rejected under similar rationale.
Claim 19 recites subject matter similar to Claim 5, and thus, is rejected under similar rationale.
Claims 7, 14, and 20 are rejected under 35 U.S.C. 103 as being unpatentable over Braganza, et al. in view of Salter, et al. and further in view of Chaudhuri (U.S. PG Publication: 2020/0279279 A1).
With respect to Claim 7, Braganza in view of Salter teach the machine learning model that is trained in real-time prior to the process of duplicate question detection in a conference via semantic natural language processing as applied to Claim 1. Braganza in view of Salter do not provide for the high-level list of NLP tasks for intent-based processing as a form of semantic analysis set forth in claim 7.
Chaudhuri, however, discloses:
transcribing speech-to-text of audio input for speech analytics, wherein the transcribed speech-to-text is combined with the meeting transcript text (generation of transcribed audio from an input stream combined from an ongoing group conversation, Paragraphs 0131, 0136, 0141-0142, and 0164; updating ongoing group transcript records to a current state is also taught by Braganza at Paragraph 0052);
analyzing the combined speech-to-text and meeting transcript text by NLP to output entities, keywords, categories, sentiment, emotion, relations, and syntax (NPL processing performed to extract "entities, keywords, categories, sentiment, emotion, relations, and syntax," Paragraph 0146); and
inputting the NLP output to intent recognition analysis to determine an intent and purpose of the asker for the question (the preceding information is used by a model to extract an intent from a user utterance, Paragraph 0146, wherein an utterance may take the form of a question/query, Paragraph 0213).
Braganza, Salter, and Chaudhuri are analogous art because they are from a similar field of endeavor in semantic analysis using machine learning. Thus, it would have been obvious to ordinary skill in the art before the effective filing date to utilize the natural language intent processing taught by Chaudhuri to allow for a speech interface in the query/question asking process of Braganza in view of Salter to provide a predictable result of better identifying what the user wants to do (Chaudhuri, Paragraph 0146).
Claim 14 recites subject matter similar to Claim 7, and thus, is rejected under similar rationale.
Claim 20 recites subject matter similar to Claim 7, and thus, is rejected under similar rationale.
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure:
Cook (U.S. PG Publication: 2023/0169276)- teaches NLP-based clustering to link together previously asked questions (Paragraphs 0041-0042).
Brannon, et al. (U.S. PG Publication: 2025/0029127 A1)- teaches a deduplication system by determining question duplicates using an NLP model (Paragraphs 0039 and 0041; Fig. 3).
Any inquiry concerning this communication or earlier communications from the examiner should be directed to JAMES S WOZNIAK whose telephone number is (571)272-7632. The examiner can normally be reached 7-3, off alternate Fridays.
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JAMES S. WOZNIAK
Primary Examiner
Art Unit 2655
/JAMES S WOZNIAK/ Primary Examiner, Art Unit 2655