DETAILED ACTION
1. This action is responsive to Application no.18/724,188 filed 9/17/2024. All claims have been examined and are currently pending.
Notice of Pre-AIA or AIA Status
2. The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA .
Information Disclosure Statement
3. The information disclosure statement (IDS) submitted is in compliance with the provisions of 37 CFR 1.97. Accordingly, the information disclosure statement is being considered by the examiner.
Specification
4. The title of the invention is not descriptive. A new title is required that is clearly indicative of the invention to which the claims are directed.
Claim Objections
5. Claim 27 is objected to as it recites: “the at least one key supplementary sentence”. There is insufficient antecedent basis for this limitation (claim 27 depends on claims 26 and 1. The key supplementary sentence limitation is recited in claim 19). Appropriate correction required.
Claim Rejections - 35 USC § 101
6. 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.
7. Claims 1, 13-14, 16, 29-30 are rejected under 35 U.S.C. 101 because the claimed invention is directed to non-statutory subject matter.
Claim 1 is rejected under 35 U.S.C. 101 because the claim as a whole, considering all claim elements both individually and in combination, does not amount to significantly more than an abstract idea.
The claim recites a series of steps and is a process.
However, The claim is directed to the abstract idea of:
Acquire user record and text; obtain a sentence of text; perform semantic matching; and determining minutes content based on record and match;
Which can fall into the grouping of mental process, concepts performed in the human mind including an observation, evaluation, judgement, opinion. The listed steps can correspond to mere observations, evaluations, and judgements; corresponding to steps that can be performed in the human mind.
The steps can also fall into the grouping of certain methods of organizing human activity, as they are steps that can be performed by a human, where a human can determine minutes (of a specific event) using the recited steps.
The claims do not recite any additional elements, and do not provide meaningful limitations to transform the abstract idea into a patent eligible application of the abstract idea such that the claims amount to significantly more that the abstract idea itself. Therefore, as there are no additional components and the limitations are abstract (recite a judicial exception), they do not integrate the judicial exception into a practical application, or provide an inventive concept, and the claim is ineligible.
Independent claims 29 and 30 recite an apparatus comprising a processor, memory, system bus,
And a non-transitory computer readable storage medium, which are recited at a high level of generality.
The additional elements or combination of elements in the claim other than the abstract idea do not amount to any more than mere instructions to implement the idea on a computer, and recitation of a generic computer structure that serves to perform generic computer functions that are well understood, routine, and conventional activities previously known to the pertinent industry. Viewed as a whole, these additional claim elements do not provide meaningful limitations to transform the abstract idea into a patent eligible application of the abstract idea such that the claims amount to significantly more that the abstract idea itself.
Claims 13-14, 16 recite
(13) determining minutes from the user record and a target sentence from a match;
(14) selecting sentences satisfying conditions according to a match;
(16) determining a target sentence based on a sentence that may be removed.
The claims do not recite any additional elements, and do not provide meaningful limitations to transform the abstract idea into a patent eligible application of the abstract idea such that the claims amount to significantly more that the abstract idea itself. Therefore, as there are no additional components and the limitations are abstract (recite a judicial exception), they do not integrate the judicial exception into a practical application, or provide an inventive concept, and the claims are also ineligible.
The additional dependent claims appear to be eligible, reciting additional limitations that integrate a judicial exception into a practical application.
Claim 2, for example, brings in additional components, a preset sentence identifier and semantic matching model, which receives inputs of the user record and text, to obtain a matching prediction result output by the semantic matching model;
Which represents limitations that are indicative of integration into a practical application (effecting a transformation), where these steps, when considered as a whole cannot be said to represent a mental process or certain method of organizing human activity.
Allowable Subject Matter
8. Claims 3-8, 11, 19-20, 23 objected to as being dependent upon a rejected base claim, but would be allowable if rewritten in independent form including all of the limitations of the base claim and any intervening claims.
Regarding claim 3, for example, Yang teaches
determining a user-record encoding feature and at least one sentence encoding feature according to the to-be-used user record, the to-be-used matching text, and the word encoding module (5 sentence encoding; document encoding; 0028: words in a sentence can be mapped to a sequence of dense vector representations)
And
[0039] A similarity metric can be determined based at least in part on the first document encoding and the second document encoding (e.g., a comparison between the document encodings, etc.). As an example, a cosine similarity cam be determined between the pooled sequence outputs corresponding to the two documents
[0036] It should be noted that by processing the documents hierarchically with the block and document encoding portions of the respective encoding submodels, the computational complexity of the semantic similarity prediction can be substantially reduced. As an example, a conventional transformer model can include an attention mechanism used for the transformer model that can be the scaled dot-product attention, which can perform transformation from a query and a set of key-value pairs to an output.
But does not specifically teach:
The minutes determining method according to claim 2, wherein the semantic matching model comprises a word encoding module, a content interaction module, and a matching prediction module, and
a process of determining the matching prediction result comprises:
determining a user-record encoding feature and at least one sentence encoding feature according to the to-be-used user record, the to-be-used matching text, and the word encoding module;
obtaining at least one content interaction feature according to the user-record encoding feature, the at least one sentence encoding feature, and the content interaction module; and
inputting the at least one content interaction feature into the matching prediction module, to obtain the matching prediction result output by the matching prediction module.
Claim Rejections - 35 USC § 103
9. 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 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.
10. 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.
11. Claims 1-2, 10, 13-14, 16, 26, 29-30 are rejected under 35 U.S.C. 103 as being unpatentable over Grueneberg et al (2018/0060289) in view of Yang et al (2022/0129638).
Regarding claim 1 Grueneberg et al (2018/0060289) teaches A minutes determining method (0016: meting summarization method), comprising:
acquiring a to-be-used user record (0020 notes…taken from each user of the meeting) and a to-be-used record text (0019 meeting audio; 24: transcribed audio);
{performing sentence segmentation processing on the to-be-used record text, to obtain at least one to-be-used sentence }
(0033: isolate the associated audio streams);
performing semantic matching processing on the to-be-used user record and the at least one to-be-used {sentence} (record text), to obtain a to-be-used semantic matching result
(0024: Then, the sample of the bag of words from the notes is matched to the bag of words of the transcribed audio that preceded the note taking, going back a predetermined amount of time in the transcribed audio (e.g., thirty seconds, two minutes, five minutes, etc.). The bag of words can be used to create a map and identify the correlation between the audio and the collected notes.
that is, a Natural Language Processor (NPL) may be used to associate the words in the notes with the words of the audio recording to synchronize the notes with the part of the audio recording that the notes were taken for.;
0025: the synchronized audio recording with the notes is analyzed to determine highlights of the meeting based on a co-occurrence of notes from the users. Generally, a “co-occurrence” has a temporal (i.e., substantially same time) meaning or in some cases may have a content meaning (i.e., what the user's are writing if the content is analyzed and related).); and
determining to-be-used minutes content according to the to-be-used user record and the to-be-used semantic matching result (0016: aggregate the meeting highlights and create a condensed meeting summarization);
but does not specifically teach where Yang et al (2022/0129638) teaches
performing sentence segmentation processing on the to-be-used record text, to obtain at least one to-be-used sentence (0005: sentence encoding);
performing semantic matching processing on the to-be-used user record and the at least one to-be-used sentence, to obtain a to-be-used semantic matching result (0004: The method can include determining, by the computing system, a similarity metric descriptive of a semantic similarity between the first document and the second document based on the first document encoding and the second document encoding).
It would have been obvious to one of ordinary skill in the art before the effective filing date to incorporate Yang to segment sentences and perform semantic matching to better determine the relationship between two documents, between user notes and audio transcription, for improved synchronization.
Grueneberg already teaches obtaining user notes, meeting transcript, and determining relationship between the two to generate meeting minutes. One could thus look to Yang to better determine the relationship (using sentence segmentation and the semantic similarity) for improved correlation and ultimately more customized user meeting notes (Grueneberg (0027))
Regarding claim 2 Yang teaches The minutes determining method according to claim 1, wherein the performing semantic matching processing on the to-be-used user record and the at least one to-be-used sentence, to obtain the to-be-used semantic matching result comprises:
determining to-be-used matching text based on the at least one to-be-used sentence and a preset sentence identifier (0005: sentence encoding; 0022: first document and a second document; [0023] Both the first and second documents can be parsed to fill the pluralities of first and second blocks in a greedy fashion. More particularly, the documents can be split into multiple textual blocks of predefined length so that each textual block can contain one or more natural sentences.);
inputting the to-be-used user record and the to-be-used matching text into a semantic matching model, to obtain a matching prediction result output by the semantic matching model (0004: The method can include determining, by the computing system, a similarity metric descriptive of a semantic similarity between the first document and the second document based on the first document encoding and the second document encoding; 0039: similarity metric); and
determining the to-be-used semantic matching result according to the matching prediction result (4; 39; [0103] At 612, the computing system can determine a similarity metric for the documents. More particularly, the computing system can determine a similarity metric descriptive of a semantic similarity between the first document and the second document based on the first document encoding and the second document encoding.).
Rejected for similar rationale and reasoning as claim 1
Regarding claim 10 Grueneberg teaches The minutes determining method according to claim 2, wherein the determining the to-be-used semantic matching result according to the matching prediction result comprises:
determining the to-be-used semantic matching result according to a to-be-used text search result and the matching prediction result, wherein the to-be-used text search result is determined according to a preset search algorithm, the to-be-used user record, and the at least one to-be-used sentence ([0024] In some embodiments, the notes of users may correspond to audio from a previous time stamp (e.g., the user takes notes on what was previously discussed). In step 103, the recorded audio is synchronized with the associated note-taking since the note-taking may have started after the associated audio one wants to capture has begun. In one exemplary embodiment, the synchronization may compensate for the delay between the audio and the notes associated with the audio by synchronizing all the notes from a same time stamp with each other and treating this collective sample as a bag of words. Then, the sample of the bag of words from the notes is matched to the bag of words of the transcribed audio that preceded the note taking, going back a predetermined amount of time in the transcribed audio (e.g., thirty seconds, two minutes, five minutes, etc.). The bag of words can be used to create a map and identify the correlation between the audio and the collected notes.
Moreover, the map can be used in a replay tool to identify the highest note taken segments during the meeting. That is, a Natural Language Processor (NPL) may be used to associate the words in the notes with the words of the audio recording to synchronize the notes with the part of the audio recording that the notes were taken for.).
Rejected for similar rationale and reasoning as claim 1 and 2 (where it was shown that Yang teaches determining the semantic matching with matching prediction result and the to-be-used sentence)
Regarding claim 13 Grueneberg and Yang teach The minutes determining method according to claim 1, wherein the at least one to-be-used sentence comprises at least one semantic matching sentence, the to-be-used semantic matching result comprises a semantic matching score between the to-be-used user record and each semantic matching sentence, and
the determining to-be-used minutes content according to the to-be-used user record and the to-be-used semantic matching result comprises:
selecting at least one target sentence satisfying a first matching condition from the at least one semantic matching sentence according to the semantic matching score between the to-be-used user record and each semantic matching sentence (Grueneberg 0006: synchronizing the recorded meeting audio of the meeting and each of the notes of each of the plurality of users based on a correlation between the time stamp, and analyzing the synchronized meeting audio and notes to determine highlights of the meeting based on a co-occurrence of notes between the plurality of users.); and
determining the to-be-used minutes content according to the to-be-used user record and the at least one target sentence
(Grueneberg [0026] Alternatively, in some embodiments, for example, a highlight may comprise a profits amount and goals for a quarter of sales based on the audio recording discussing profits and goals and each of the user notes referring to the profits and goals for the quarter. That is, a highlight of profits and goals is determined based on a co-occurrence of the notes of multiple users discussing profits and goals as well as the audio recording discussing the same. However, if the audio recording is discussing profits and goals and notes of a user recite “pick up bread at store”, a highlight is not created because “pick up bread at the store” is not in the audio recording and does not co-occur in other users notes. In other words, the synchronized audio and notes from multiple meeting attendees are analyzed in step 104, looking for co-occurring note taking, to determine the meeting highlights. Thus, a meeting highlight may be when most notes are taken;
0016: aggregate the meeting highlights and create a condensed meeting summarization.).
Rejected for similar rationale and reasoning as claim 1, which demonstrates
Where Grueneberg does not specifically teach but Yang teaches
wherein the at least one to-be-used sentence comprises at least one semantic matching sentence, the to-be-used semantic matching result comprises a semantic matching score between the to-be-used user record and each semantic matching sentence (semantic similarity/matching between 2 documents (user notes and transcription), using portions of the documents/sentence);
and where it would be obvious to incorporate Yang for improved synchronization between texts and more customized user notes.
Regarding claim 14 Grueneberg and Yang teach The minutes determining method according to claim 13, wherein the selecting at least one target sentence satisfying a first matching condition from the at least one semantic matching sentence according to the semantic matching score between the to-be-used user record and each semantic matching sentence comprises:
selecting at least one primary sentence satisfying a second matching condition from the at least one semantic matching sentence according to the semantic matching score between the to-be-used user record and each semantic matching sentence (Grueneberg [0027] In step 106, each of the highlights determined in step 105 is aligned with a topic of the meeting or with a topic of a meeting agenda.); and
selecting the at least one target sentence satisfying a third matching condition from the at least one primary sentence according to a semantic matching score between the to-be-used user record and each primary sentence (Grueneberg [0027] In step 106, each of the highlights determined in step 105 is aligned with a topic of the meeting or with a topic of a meeting agenda. For example, if there is no agenda, the topics of the meeting can be extracted from the audio recording using a NPL. The highlights can be aligned with the topics such that a user can later access the highlights of the meeting based on a topic. For example, if a user is only part of the marketing department but not part of the sales, the user may wish only to review highlights pertaining to the marketing topics and not the sales topics. Alternatively, the highlights may be aligned to each topic listed in the agenda. Thus, the users can select the highlights based on the agenda)
Rejected for similar rationale and reasoning as claim 1 and 13 above
Regarding claim 16 Grueneberg teaches The minutes determining method according to claim 13, wherein a process of determining the at least one target sentence comprises:
selecting a to-be-deleted sentence satisfying a first deletion condition from at least one candidate sentence according to a semantic matching score between the to-be-used user record and each candidate sentence, to obtain a first selection result (Grueneberg 0026: however, if the audio recording is discussing profits and goals and notes of a user recite “pick up bread at store”, a highlight is not created because “pick up bread at the store” is not in the audio recording and does not co-occur in other users notes.; 0027: For example, if a user is only part of the marketing department but not part of the sales, the user may wish only to review highlights pertaining to the marketing topics and not the sales topics.); and
determining the at least one target sentence based on the first selection result and the at least one candidate sentence (Grueneberg 0016: aggregate the meeting highlights and create a condensed meeting summarization).
Rejected for similar rationale and reasoning as claim 1 and 13 above
Regarding claim 26 Grueneberg teaches The minutes determining method according to claim 1, wherein the determining to-be-used minutes content according to the to-be-used user record and the to-be-used semantic matching result comprises:
determining prompt information according to the to-be-used user record and the to-be-used semantic matching result (27); and
displaying the prompt information to a user, wherein the prompt information provide a reference to writing the to-be-used minutes content by the user
([0027] In step 106, each of the highlights determined in step 105 is aligned with a topic of the meeting or with a topic of a meeting agenda. For example, if there is no agenda, the topics of the meeting can be extracted from the audio recording using a NPL. The highlights can be aligned with the topics such that a user can later access the highlights of the meeting based on a topic. For example, if a user is only part of the marketing department but not part of the sales, the user may wish only to review highlights pertaining to the marketing topics and not the sales topics. Alternatively, the highlights may be aligned to each topic listed in the agenda. Thus, the users can select the highlights based on the agenda.).
Regarding claim 29 Grueneberg and Yang teach An apparatus (Grueneberg figures 1-4) comprising:
a processor, a memory, and a system bus, wherein the processor and the memory are connected through the system bus (Grueneberg 0016: As shown in at least FIG. 2, one or more computers of a computer system 12 according to an embodiment of the present invention can include a memory 28 having instructions stored in a storage system to perform the steps of FIG. 1.); and
the memory is configured to store one or more programs comprising instructions, and the instructions, when executed by the processor, cause the processor to:
acquire a to-be-used user record and a to-be-used record text;
perform sentence segmentation processing on the to-be-used record text, to obtain at least one to-be-used sentence;
perform semantic matching processing on the to-be-used user record and the at least one to-be-used sentence, to obtain a to-be-used semantic matching result; and
determine to-be-used minutes content according to the to-be-used user record and the to-be-used semantic matching result.
Claim recites limitations similar to claim 1 and is rejected for similar rationale and reasoning
Regarding claim 30 Grueneberg and Yang teach A non-transitory computer-readable storage medium having instructions stored therein, where the instructions, when run on a terminal device, cause the terminal device (Grueneberg fig 1-4; 0016) to:
acquire a to-be-used user record and a to-be-used record text;
perform sentence segmentation processing on the to-be-used record text, to obtain at least one to-be-used sentence;
perform semantic matching processing on the to-be-used user record and the at least one to-be-used sentence, to obtain a to-be-used semantic matching result; and
determine to-be-used minutes content according to the to-be-used user record and the to-be-used semantic matching result.
Claim recites limitations similar to claim 1 and is rejected for similar rationale and reasoning
Conclusion
12. The prior art made of record and not relied upon is considered pertinent to applicant's disclosure: See PTO-892.
Tay et al (2022/0254348)
Abstract: A method and apparatus for automatically generating a meeting summary is disclosed herein. Meeting audio is recorded and converted into a text-based transcript. Handwritten meeting notes are converted into notes text. The transcript and notes text are correlated to provide correlated meeting text. Meeting topics are determined from the correlated meeting text. A meeting summary is generated from the meeting topics.
Asthana et al (2022/0109585)
Abstract: In an approach to customizing meeting notes, a computer receives audio input of a virtual meeting, converts the audio input to text, and displays the text to a plurality of meeting participants. A computer receives highlighted phrases of the text from the plurality of meeting participants and determines a highlighting frequency of each of the highlighted phrases. A computer determines phrases with a highlighting frequency greater than a pre-defined threshold. A computer orders the phrases based on a chronological order of the phrases in the audio input. A computer determines preferences of a first meeting participant associated with a meeting summary. A computer generates a customized summary of the virtual meeting for the first meeting participant of the plurality of meeting participants based on the ordered phrases with a high frequency of highlighting and on the preferences. A computer transmits the customized summary to the first meeting participant.
Zeng et al (2021/0375291)
Abstract: Attributes of electronic content from a meeting are identified and evaluated to determine whether sub-portions of the electronic content should or should not be attributed to a user profile. Upon determining that the sub-portion should be attributed to a user profile, attributes of the sub-portion of electronic content are compared to attributes of stored user profiles. A probability that the sub-portion corresponds to at least one stored user profile is calculated. Based on the calculated probability, the sub-portion is attributed to a stored user profile or a guest user profile.
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/SHAUN ROBERTS/Primary Examiner, Art Unit 2655
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