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 .
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 Amendment
This Action is responsive to the Applicant’s Amendment/Remarks filed on 10/14/2025. In the Amendment, applicant amended claims 1-2, 11-12 and 19. Claims 31-36 are newly added. Claims 4-7, 9, 14-15, 17-18, 20-24, 28 and 30 are cancelled. As necessitated by the Amendment, Examiner hereby respectfully maintains double patenting rejection to claims 1-3, 8, 10-13, 16, 19, 25-27, 29 and 31-36 and maintains 35 U.S.C § 101 rejections to claims 1-3, 8, 10-13, 16, 19, 25-27, 29 and 31-36.
As to Arguments and Remarks filed in the Amendment, please see Examiner’s responses shown after Rejections - 35 U.S.C § 103.
Please note claims 1-3, 8, 10-13, 16, 19, 25-27, 29 and 31-36 are pending.
Claims 4-7, 9, 14-15, 17-18, 20-24, 28 and 30 are cancelled.
Examiner Note: Case would be allowable if the applicant clarify types of the records that need to be assigned (i.e., conversation, speech, chat, voice, etc. ) and also including the features of verify distance associated with new candidate group of semantical similar records base on plurality thresholds.
Information Disclosure Statement
The information disclosure statement (IDS) filed on 09/02/2025 and 10/02/2025 has been considered (see form-1449, MPEP 609).
Double Patenting
The nonstatutory double patenting rejection is based on a judicially created doctrine grounded in public policy (a policy reflected in the statute) so as to prevent the unjustified or improper timewise extension of the “right to exclude” granted by a patent and to prevent possible harassment by multiple assignees. A nonstatutory double patenting rejection is appropriate where the conflicting claims are not identical, but at least one examined application claim is not patentably distinct from the reference claim(s) because the examined application claim is either anticipated by, or would have been obvious over, the reference claim(s). See, e.g., In re Berg, 140 F.3d 1428, 46 USPQ2d 1226 (Fed. Cir. 1998); In re Goodman, 11 F.3d 1046, 29 USPQ2d 2010 (Fed. Cir. 1993); In re Longi, 759 F.2d 887, 225 USPQ 645 (Fed. Cir. 1985); In re Van Ornum, 686 F.2d 937, 214 USPQ 761 (CCPA 1982); In re Vogel, 422 F.2d 438, 164 USPQ 619 (CCPA 1970); In re Thorington, 418 F.2d 528, 163 USPQ 644 (CCPA 1969).
A timely filed terminal disclaimer in compliance with 37 CFR 1.321(c) or 1.321(d) may be used to overcome an actual or provisional rejection based on nonstatutory double patenting provided the reference application or patent either is shown to be commonly owned with the examined application, or claims an invention made as a result of activities undertaken within the scope of a joint research agreement. See MPEP § 717.02 for applications subject to examination under the first inventor to file provisions of the AIA as explained in MPEP § 2159. See MPEP § 2146 et seq. for applications not subject to examination under the first inventor to file provisions of the AIA . A terminal disclaimer must be signed in compliance with 37 CFR 1.321(b).
The USPTO Internet website contains terminal disclaimer forms which may be used. Please visit www.uspto.gov/patent/patents-forms. The filing date of the application in which the form is filed determines what form (e.g., PTO/SB/25, PTO/SB/26, PTO/AIA /25, or PTO/AIA /26) should be used. A web-based eTerminal Disclaimer may be filled out completely online using web-screens. An eTerminal Disclaimer that meets all requirements is auto-processed and approved immediately upon submission. For more information about eTerminal Disclaimers, refer to www.uspto.gov/patents/process/file/efs/guidance/eTD-info-I.jsp.
Claims 1-3, 8, 10-13, 16, 19, 25-27, 29 and 31-36 are rejected on the ground of nonstatutory double patenting as being unpatentable over claims 1-17 of U.S. Patent No. 12,079,224. Although the conflicting are not patentably distinct from each other because since the claims of the Patent No. 12,079,224 contains every element of the claims of the instant application, and as such, anticipate the claims of the instant application 18/780357. (See table below).
Instant Application claim 1
Patent No. 12,079,224 claim 1
A method comprising: identifying a set of out-of-domain records not classifiable into one of a plurality of existing groups of semantically similar records;
determining a new candidate group of semantically similar records comprising a subset of the set of out-of-domain records, wherein the new candidate group is distinct from the plurality of existing groups of semantically similar records;
automatically assigning one or more records of the plurality of unassigned records to the new candidate group when a respective semantic representation of one or more text fields associated with a respective record of the one or more records is within a classification threshold a reference semantic representation for the new candidate group; and automatically updating, at a database system, the one or more records associated with the one or more records to include metadata identifying assignment the new candidate group.
A method comprising: identifying a set of out-of-domain conversations from among a plurality of unassigned conversations using a plurality of existing groups of semantically similar conversations, wherein the set of out-of-domain conversations comprises a first subset of the plurality of unassigned conversations; determining, based upon the plurality of unassigned conversations, a new candidate group of semantically similar conversations comprising a subset of the plurality of unassigned conversations, the subset of the plurality of unassigned conversations comprising a second subset of the set of out-of-domain conversations, wherein the new candidate group is distinct from the plurality of existing groups of semantically similar conversations, wherein a respective existing group of the plurality of existing groups comprises a distinct subset of conversations assigned to the respective existing group based on a respective representative utterance associated with the respective conversation of the distinct subset of conversations; determining a clustering performance metric associated with the new candidate group of semantically similar conversations based on a relationship between a reference semantic representation for the new candidate group of semantically similar conversations and respective semantic representations for the plurality of existing groups of semantically similar conversations; and when the clustering performance metric is greater than a threshold: automatically assigning one or more conversations of the plurality of unassigned conversations to the new candidate group based on a relationship between a respective representative utterance associated with a respective conversation of the one or more conversations and the reference semantic representation for the new candidate group; and automatically updating, at a database system, one or more records associated with the one or more conversations to include metadata identifying the new candidate group.
Claims 1-9 of Patent No. 12,079,224 satisfies all the elements of claims 1-3, 8, 10 and 25-27, 29 and 31-36 of the instant application, and as such, anticipates the claims of instant application.
Instant Application claim 11
Patent No. 12,079,224 claim 10
A method comprising: identifying a set of out-of-domain records not classifiable into one of a plurality of existing groups of semantically similar records;
determining a new candidate group of semantically similar records comprising a subset of the set of out-of-domain records, wherein the new candidate group is distinct from the plurality of existing groups of semantically similar records;
automatically assigning one or more records of the plurality of unassigned records to the new candidate group when a respective semantic representation of one or more text fields associated with a respective record of the one or more records is within a classification threshold a reference semantic representation for the new candidate group; and automatically updating, at a database system, the one or more records associated with the one or more records to include metadata identifying assignment the new candidate group.
At least one non-transitory machine-readable storage medium that provides instructions that, when executed by at least one processor, are configurable to cause the at least one processor to perform operations comprising: identifying a set of out-of-domain conversations from among a plurality of unassigned conversations using a plurality of existing groups of semantically similar conversations, wherein the set of out-of-domain conversations comprises a first subset of the plurality of unassigned conversations; determining, based upon the plurality of unassigned conversations, a new candidate group of semantically similar conversations comprising a subset of the plurality of unassigned conversations, the subset of the plurality of unassigned conversations comprising a second subset of the set of out-of-domain conversations, wherein the new candidate group is distinct from the plurality of existing groups of semantically similar conversations, wherein a respective existing group of the plurality of existing groups comprises a distinct subset of conversations assigned to the respective existing group based on a respective representative utterance associated with the respective conversation of the distinct subset of conversations; determining a clustering performance metric associated with the new candidate group of semantically similar conversations based on a relationship between a reference semantic representation for the new candidate group of semantically similar conversations and respective semantic representations for the plurality of existing groups of semantically similar conversations; and when the clustering performance metric is greater than a threshold: automatically assigning one or more conversations of the plurality of unassigned conversations to the new candidate group based on a relationship between a respective representative utterance associated with a respective conversation of the one or more conversations and the reference semantic representation for the new candidate group; and automatically updating one or more records associated with the one or more conversations to include metadata identifying the new candidate group.
Claims 10-16 of Patent No. 12,079,224 satisfies all the elements of claims 11-13 and 16 of the instant application, and as such, anticipates the claims of instant application.
Instant Application claim 19
Patent No. 12,079,224 claim 17
A method comprising: identifying a set of out-of-domain records not classifiable into one of a plurality of existing groups of semantically similar records;
determining a new candidate group of semantically similar records comprising a subset of the set of out-of-domain records, wherein the new candidate group is distinct from the plurality of existing groups of semantically similar records;
automatically assigning one or more records of the plurality of unassigned records to the new candidate group when a respective semantic representation of one or more text fields associated with a respective record of the one or more records is within a classification threshold a reference semantic representation for the new candidate group; and automatically updating, at a database system, the one or more records associated with the one or more records to include metadata identifying assignment the new candidate group.
A computing system comprising: at least one non-transitory machine-readable storage medium that stores software; and at least one processor, coupled to the at least one non-transitory machine-readable storage medium, to execute the software that implements a conversation mining service and that is configurable to perform operations comprising: identifying a set of out-of-domain conversations from among a plurality of unassigned conversations using a plurality of existing groups of semantically similar conversations, wherein the set of out-of-domain conversations comprises a first subset of the plurality of unassigned conversations; determining, based upon the plurality of unassigned conversations, a new candidate group of semantically similar conversations comprising a subset of the plurality of unassigned conversations, the subset of the plurality of unassigned conversations comprising a second subset of the set of out-of-domain conversations, wherein the new candidate group is distinct from the plurality of existing groups of semantically similar conversations, wherein a respective existing group of the plurality of existing groups comprises a distinct subset of conversations assigned to the respective existing group based on a respective representative utterance associated with the respective conversation of the distinct subset of conversations; determining a clustering performance metric associated with the new candidate group of semantically similar conversations based on a relationship between a reference semantic representation for the new candidate group of semantically similar conversations and respective semantic representations for the plurality of existing groups of semantically similar conversations; and when the clustering performance metric is greater than a threshold: automatically assigning one or more conversations of the plurality of unassigned conversations to the new candidate group based on a relationship between a respective representative utterance associated with a respective conversation of the one or more conversations and the reference semantic representation for the new candidate group; and automatically updating one or more records associated with the one or more conversations to include metadata identifying the new candidate group.
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 1-3, 8, 10-13, 16, 19, 25-27, 29 and 31-36 are rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more. The Claim recites the language of “ identifying a set of out-of-domain records not classifiable into one of a plurality of existing groups of semantically similar records;
determining a new candidate group of semantically similar records comprising a subset of the set of out-of-domain records, wherein the new candidate group is distinct from the plurality of existing groups of semantically similar records;
automatically assigning one or more records of the plurality of unassigned records to the new candidate group when a respective semantic representation of one or more text fields associated with a respective record of the one or more records is within a classification threshold a reference semantic representation for the new candidate group; and automatically updating, at a database system, the one or more records associated with the one or more records to include metadata identifying assignment the new candidate group.”
Claim 1 recites the limitation of “identifying a set of out-of-domain records not classifiable into one of a plurality of existing groups of semantically similar records”, as drafted, is a process that, under its broadest reasonable interpretation, covers performance of the limitation in the mind but for the recitation of generic computer components. That is nothing in the claim element precludes the step from practically being performed in the mind. For example, “identifying” in the context of this claim encompasses the user manually recognizing records. Similarly, the limitation of determining a new candidate group of semantically similar records comprising a subset of the set of out-of-domain records, wherein the new candidate group is distinct from the plurality of existing groups of semantically similar records, as drafted, is a process that, under its broadest reasonable interpretation, covers performance of the limitation in the mind but for the recitation of generic computer components. For example, but for the “identifying” in the context of this claim encompasses the user to determine the data records. Similarly, the limitation of automatically assigning one or more records of the plurality of unassigned records to the new candidate group when a respective semantic representation of one or more text fields associated with a respective record of the one or more records is within a classification threshold a reference semantic representation for the new candidate group, as drafted, is a process that, under its broadest reasonable interpretation, covers performance of the limitation in the mind but for the recitation of generic computer components. For example, “assigning” in the context of this claim encompasses the user manually arranging records. Also Similarly, the limitation of automatically updating, at a database system, the one or more records associated with the one or more records to include metadata identifying assignment the new candidate group, as drafted, is a process that, under its broadest reasonable interpretation, covers performance of the limitation in the mind but for the recitation of generic computer components. For example, “updating” in the context of this claim encompasses the user manually change/modify the information. If a claim limitation, under its broadest reasonable interpretation, covers performance of the limitation in the mind but for the recitation of generic computer components, then it falls within the “Human Activity” grouping of abstract ideas. Accordingly, the claim recites an abstract idea.
This judicial exception is not integrated into a practical application. In particular, the claim only recites one additional element – using a processor or non-transitory machine readable storage medium to perform the identifying, determining, assigning, updating steps (Note that, the steps of identifying, determining, assigning, updating can be interprets as gathering information which is abstract ideas falls within the “Human Activity” as stated above”). The processor and machine readable storage medium in those steps is recited at a high-level of generality (i.e., as a generic processor performing a generic computer function of identifying and updating records) such that it amounts no more than mere instructions to apply the exception using a generic computer component. Accordingly, this additional element does not integrate the abstract idea into a practical application because it does not integrate the abstract idea into a practical application because it does not impose any meaningful limits on practicing the abstract idea. The claim is directed to an abstract idea.
The claim does not include additional elements that are sufficient to amount to significantly more than the judicial exception. As discussed above with respect to integration of the abstract idea into a practical application, the additional element of using a processor the identifying, determining, assigning, updating steps amounts to no more than mere instructions to apply the exception using a generic computer component. Mere instructions to apply an exception using a generic computer component cannot provide an inventive concept. The claim is not patent eligible.
Claim 2 is dependent on independent claim 1 and includes all the limitations of claim 1. Claim 2 recites “the set of out-of-domain records comprises a first subset of the plurality of unassigned records; and determining the new candidate group comprises identifying the new candidate group of semantically similar records comprising a second subset of the set of out-of-domain records”. The claim language provides only further determining which is directed towards the abstract idea and does not amount to significantly more. The claim does not include additional elements that are sufficient to amount to significantly more than the judicial exception because the additional elements when considered both individually and as an ordered combination do not amount to significantly more than the abstract idea.
Claim 3 is dependent on independent claim 2 and includes all the limitations of claims 2 and 1. Claim 3 recites “identifying the new candidate group comprises clustering the second subset of the set of out-of-domain records into the new candidate group based on a respective semantic representation of one or more text fields associated with the respective out-of-domain records of the second subset of the set of out-of-domain records”. The claim language provides only further identifying information which is directed towards the abstract idea and does not amount to significantly more. The claim does not include additional elements that are sufficient to amount to significantly more than the judicial exception because the additional elements when considered both individually and as an ordered combination do not amount to significantly more than the abstract idea.
Claim 8 is dependent on independent claim 1 and includes all the limitations of claim 1. Claim 8 recites “an assignment algorithm to cluster records into a set of cluster groups comprising the plurality of existing groups and the new candidate group when a clustering performance metric is greater than a cluster group inclusion threshold, resulting in an updated assignment algorithm, wherein automatically assigning the one or more records comprises automatically assigning the one or more records to the new candidate group based on the semantic representation of one or more text fields associated with the respective record of the one or more records using the updated assignment algorithm”. The claim language provides only further assigning records which is directed towards the abstract idea and does not amount to significantly more. The claim does not include additional elements that are sufficient to amount to significantly more than the judicial exception because the additional elements when considered both individually and as an ordered combination do not amount to significantly more than the abstract idea.
Claim 10 is dependent on independent claim 1 and includes all the limitations of claim 1. Claim 10 recites “determining a performance metric associated with the new candidate group after automatically assigning the one or more records, wherein the performance metric is influenced by one or more performance metrics associated with the one or more records”. The claim language provides only further determining new candidate group which is directed towards the abstract idea and does not amount to significantly more. The claim does not include additional elements that are sufficient to amount to significantly more than the judicial exception because the additional elements when considered both individually and as an ordered combination do not amount to significantly more than the abstract idea.
Claim 25 is dependent on independent claim 1 and includes all the limitations of claim 1. Claim 25 recites “providing a graphical user interface (GUD display including graphical indicia of the plurality of existing groups and the new candidate group”. The claim language provides only further graphical user interface which is directed towards the abstract idea and does not amount to significantly more. The claim does not include additional elements that are sufficient to amount to significantly more than the judicial exception because the additional elements when considered both individually and as an ordered combination do not amount to significantly more than the abstract idea.
Claim 26 is dependent on independent claim 25 and includes all the limitations of claims 25 and 1. Claim 26 recites “providing a group analysis GUI display comprising a listing of the respective semantic representation of the one or more text fields associated with the respective record of the one or more records in response to user selection of the new candidate group on the GUI display”. The claim language provides only further group analysis which is directed towards the abstract idea and does not amount to significantly more. The claim does not include additional elements that are sufficient to amount to significantly more than the judicial exception because the additional elements when considered both individually and as an ordered combination do not amount to significantly more than the abstract idea.
Claim 27 is dependent on independent claim 26 and includes all the limitations of claims 26, 25 and 1. Claim 27 recites “the plurality of existing groups comprise a plurality of existing contact reason cluster groups; the new candidate group comprises a new contact reason cluster group; and the group analysis GUI display comprises a listing of the representative utterances associated with conversations assigned to the new contact reason cluster group”. The claim language provides only further contact groups which is directed towards the abstract idea and does not amount to significantly more. The claim does not include additional elements that are sufficient to amount to significantly more than the judicial exception because the additional elements when considered both individually and as an ordered combination do not amount to significantly more than the abstract idea.
Claim 29 is dependent on independent claim 10 and includes all the limitations of claims 10 and 1. Claim 29 recites “a graphical user interface (GUI) display including graphical indicia of the performance metric associated with the new candidate group and respective performance metrics associated with the plurality of existing groups”. The claim language provides only further graphical user interface which is directed towards the abstract idea and does not amount to significantly more. The claim does not include additional elements that are sufficient to amount to significantly more than the judicial exception because the additional elements when considered both individually and as an ordered combination do not amount to significantly more than the abstract idea.
Claim 31 is dependent on independent claim 1 and includes all the limitations of claim 1. Claim 31 recites “determining the new candidate group comprises automatically discovering the new candidate group distinct from the plurality of existing groups of semantically similar records based on semantic content of the subset of the set of out-of-domain records; and the subset of the set of out-of-domain records comprises new records more recent than identification of the plurality of existing groups”. The claim language provides only further determining the new candidate group which is directed towards the abstract idea and does not amount to significantly more. The claim does not include additional elements that are sufficient to amount to significantly more than the judicial exception because the additional elements when considered both individually and as an ordered combination do not amount to significantly more than the abstract idea.
Claim 32 is dependent on independent claim 31 and includes all the limitations of claims 31 and 1. Claim 32 recites “receiving, at the database
system, at least a threshold number of the new records after the identification of the plurality of existing groups, wherein determining the new candidate group comprises automatically discovering the new candidate group based on the semantic content of the subset of the set of out-of-domain records in response to receiving at least the threshold number of the new records at the database system”. The claim language provides only further receiving records which is directed towards the abstract idea and does not amount to significantly more. The claim does not include additional elements that are sufficient to amount to significantly more than the judicial exception because the additional elements when considered both individually and as an ordered combination do not amount to significantly more than the abstract idea.
Claim 33 is dependent on independent claim and includes all the limitations of claims 1. Claim 33 recites “verifying an intra-cluster distance associated with the new
candidate group of semantically similar records is less than a second threshold and an inter-cluster distance between the new candidate group and the plurality of existing groups is greater than a third threshold prior to automatically assigning the one or more records”. The claim language provides only further verifying new group which is directed towards the abstract idea and does not amount to significantly more. The claim does not include additional elements that are sufficient to amount to significantly more than the judicial exception because the additional elements when considered both individually and as an ordered combination do not amount to significantly more than the abstract idea.
Claim 34 is dependent on independent claim 1 and includes all the limitations of claim 1. Claim 34 recites “subset of the set of out-of-domain
records comprises a unique subset of the set of out-of-domain unassigned conversations input to an unsupervised model to fit the unsupervised model to the set of out-of-domain unassigned conversations”. The claim language provides only further subset of the records which is directed towards the abstract idea and does not amount to significantly more. The claim does not include additional elements that are sufficient to amount to significantly more than the judicial exception because the additional elements when considered both individually and as an ordered combination do not amount to significantly more than the abstract idea.
Claim 35 is dependent on independent claim 1 and includes all the limitations of claim 1. Claim 35 recites “determining, at the database system, the respective semantic representation of one or more text fields associated with the respective record based on a combination of text values for the one or more text fields of the respective record by inputting the text values for the one or more text fields of the respective record into a summarization model”. The claim language provides only further determining text field which is directed towards the abstract idea and does not amount to significantly more. The claim does not include additional elements that are sufficient to amount to significantly more than the judicial exception because the additional elements when considered both individually and as an ordered combination do not amount to significantly more than the abstract idea.
Claim 36 is dependent on independent claim 1 and includes all the limitations of claim 1. Claim 36 recites “determining, at the database system, the respective semantic representation of one or more text fields associated with the respective record based on a combination of text values for the one or more text fields of the respective record by inputting the text values for the one or more text fields of the respective record into an encoder model”. The claim language provides only further determining text fields which is directed towards the abstract idea and does not amount to significantly more. The claim does not include additional elements that are sufficient to amount to significantly more than the judicial exception because the additional elements when considered both individually and as an ordered combination do not amount to significantly more than the abstract idea.
Regarding claims 11-13 and 16: are essentially the same as claims 1-3, 8, 10 and 21-30 except that they set forth the claimed invention as a non transitory machine readable storage medium rather than a method respectively and correspondingly, therefore are rejected under the same reasons set forth in rejections of claims 1-3, 8, 10 and 21-30.
Regarding claim 19: are essentially the same as claim 1 except that they set forth the claimed invention as a system rather than a method respectively and correspondingly, therefore are rejected under the same reasons set forth in rejections of claim 1.
Accordingly, the claims 1-3, 8, 10-13, 16, 19 and 21-30 are not patent eligible.
Examiner Notes
Examiner cites particular columns, paragraphs, figures and line numbers in the references as applied to the claims below for the convenience of the applicant. Although the specified citations are representative of the teachings in the art and are applied to the specific limitations within the individual claim, other passages and figures may apply as well. It is respectfully requested that, in preparing responses, the applicant fully consider the references in their entirety as potentially teaching all or part of the claimed invention, as well as the context of the passage as taught by the prior art or disclosed by the examiner.
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.
The text of those sections of Title 35, U.S. Code not included in this action can be found in a prior Office action.
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.
Claims 1-3, 8, 10-13, 16, 19, 25-27, 29, 31-34 are rejected under 35 U.S.C. 103 as being unpatentable over Chaudhuri et al. (US PGPUB 2018/0174600, hereinafter Chaudhuri), in view of Kumar et al. (US PGPUB 2023/0252980, hereinafter Kumar).
As per as claim 1, Chaudhuri discloses:
(Currently Amended) A method comprising:
identifying a set of out-of-domain records from among a plurality of unassigned records using a plurality of existing groups of semantically similar records (Chaudhuri, e.g., fig. 4, associating with texts description, [0004], [0016], “…determining temporal positions of separate voices in the video using clusters of voice features…speaker label data for the video is generated based on the correlation…” and further see [0032-0035]);
determining a new candidate group of semantically similar records comprising a subset of the set of out-of-domain records, wherein the new candidate group is distinct from the plurality of existing groups of semantically similar records (Chaudhuri, e.g., [0032-0035], [0042], “…performs voice clustering, i.e., groups detected voices together that are determined to belong to the same speaker…determines which segments of speech in the audio for a video belong to the same speaker... the voice clusterer arrives at a set of candidate voices (i.e., voice clusters) that are most likely to be unique and from separate speakers, although in some cases the voice clusterer may overestimate and determine that there are more candidate voices than actual voices in the video…” and [0113-0114], "the voice detection subsystem determines the set of four different voices A, B, C, and D);
automatically assigning one or more records of the plurality of unassigned records to the new candidate group when a respective semantic representation of one or more text fields associated with a respective record of the one or more records is within a classification threshold of a reference semantic representation for the new candidate group (Chaudhuri, e.g., [0016], [0034], “…compare the values in the results to see if a threshold value or multiple threshold values are met, indicating a that the two detected faces are likely of the same speaker…” and see [0040-0042], [0085], [0127], “…aggregate value exceeds a threshold…performs voice clustering, i.e., groups detected voices together that are determined to belong to the same speaker…analyzes the similarity of the voice features for the remaining segments of voices, and combines those that are within a threshold level of similarity…”); and
automatically updating, at a database system, the one or more records to include metadata identifying assignment the new candidate group (Chaudhuri, e.g., [0084], [0130] “…the machine learning model is trained to determine if the speaker changes between the two windows (i.e., the left window has one speaker, and the right window has another speaker). The windows may be “slid” over the speech segment in specified intervals, e.g., 0.1 seconds. After determining that a speaker turn has occurred or has likely occurred (e.g., with a determination above a threshold amount), the speech segment may be split at the determination point into separate speech segments…”).
To make records clearer regarding to the features of “assigning one or more records of the plurality of unassigned records to the new candidate group with a respective record of the one or more records is within a classification threshold” and “automatically updating, at a database system, the one or more records to include metadata identifying assignment the new candidate group” (although as stated above Chaudhuri functional disclose the features of grouping/clustering/assigning the records (Chaudhuri, e.g., [0042], [0085])).
However Kumar, in an analogous art, discloses “assigning one or more records of the plurality of unassigned records to the new candidate group with a respective record of the one or more records is within a classification threshold” (Kumar, e.g., [0063-0071], “… (e.g., national politics, news stories) may be assigned a label indicating the segment is “not relevant,” “not likely relevant,” “filtered,” etc. It is understood that the name of the label is irrelevant, as long as the result is that the corresponding segment is omitted from mapping to a candidate transaction … a relevancy threshold may be different according to a communication channel over which a conversation is conducted... assign a “not relevant” label to a conversation segment in a text SMS message that includes semantic content associated with a participant’s location …”);
“automatically updating, at a database system, the one or more records to include metadata identifying assignment the new candidate group” (Kumar, e.g., [0036-0037], “...classify conversations and portions of conversations as being related to one or more of hundreds or thousands of potential categories... conversation mapping engine 113 maps the segments to candidate transactions based on the semantic content and/or metadata associated with the segments...” and [0063-0068], (assigning/label new data to category/groups) and [0080], [0098-0099], for updating ). Thus, it would have been obvious to one of ordinary skill in the art BEFORE the effective filling date of the claimed invention to combine the teaching of Kumar and Chaudhuri to provide the amount of data that would need to be searched makes it impractical for a human user or group of users to perform the search and identifying a relationship of one conversation on one communication channel, which does not name a particular transaction, to another conversation on another communication channel that may or may not name the particular transaction (Higgins, e.g., [0002-0004]).
As per as claim 2, the combination Kumar and Chaudhuri disclose:
(Original) The method of claim 1, wherein:
the set of out-of-domain records comprises a first subset of the plurality of unassigned records not within the classification threshold of the plurality of existing group (Kumar, e.g., [0063-0071], “… (e.g., national politics, news stories) may be assigned a label indicating the segment is “not relevant,” “not likely relevant,” “filtered,” etc. It is understood that the name of the label is irrelevant, as long as the result is that the corresponding segment is omitted from mapping to a candidate transaction … a relevancy threshold may be different according to a communication channel over which a conversation is conducted... assign a “not relevant” label to a conversation segment in a text SMS message that includes semantic content associated with a participant’s location …”) and (Chaudhuri, e.g., [0038-0042], “…analyzes the similarity of the voice features for the remaining segments of voices, and combines those that are within a threshold level of similarity…”); and
determining the new candidate group comprises identifying the new candidate group of semantically similar records comprising a second subset of the set of out-of-domain records (Chaudhuri, e.g., [0032-0035], [0042], “…performs voice clustering, i.e., groups detected voices together that are determined to belong to the same speaker…determines which segments of speech in the audio for a video belong to the same speaker... the voice clusterer arrives at a set of candidate voices (i.e., voice clusters) that are most likely to be unique and from separate speakers, although in some cases the voice clusterer may overestimate and determine that there are more candidate voices than actual voices in the video…” and [0113-0114], "the voice detection subsystem determines the set of four different voices A, B, C, and D) and further see (Kumar, e.g., [0063-0071]).
As per a claim 3, the combination Kumar and Chaudhuri disclose:
(Original) The method of claim 2, wherein identifying the new candidate group comprises clustering the second subset of the set of out-of-domain records into the new candidate group based on a respective semantic representation of one or more text fields associated with the respective out-of-domain records of the second subset of the set of out-of- domain records (Chaudhuri, e.g., [0032-0035], [0042], “…performs voice clustering, i.e., groups detected voices together that are determined to belong to the same speaker…determines which segments of speech in the audio for a video belong to the same speaker... the voice clusterer arrives at a set of candidate voices (i.e., voice clusters) that are most likely to be unique and from separate speakers, although in some cases the voice clusterer may overestimate and determine that there are more candidate voices than actual voices in the video…” and [0113-0114], "the voice detection subsystem determines the set of four different voices A, B, C, and D).
As per as claim 8, the combination Kumar and Chaudhuri disclose:
(Original) The method of claim 1, further comprising configuring an assignment algorithm to cluster records into a set of cluster groups comprising the plurality of existing groups and the new candidate group when a clustering performance metric is greater than a cluster group inclusion threshold, resulting in an updated assignment algorithm, wherein automatically assigning the one or more records comprises automatically assigning the one or more records to the new candidate group based on the semantic representation of one or more text fields associated with the respective record of the one or more records using the updated assignment algorithm (Kumar, e.g., [0036-0037], “...classify conversations and portions of conversations as being related to one or more of hundreds or thousands of potential categories... conversation mapping engine 113 maps the segments to candidate transactions based on the semantic content and/or metadata associated with the segments...” and [0063-0068], (assigning/label new data to category/groups) and [0080] for updating ) and (Chaudhuri, e.g., [0034], “…compare the values in the results to see if a threshold value or multiple threshold values are met, indicating a that the two detected faces are likely of the same speaker…” and see [0040-0042], “…aggregate value exceeds a threshold…performs voice clustering, i.e., groups detected voices together that are determined to belong to the same speaker…analyzes the similarity of the voice features for the remaining segments of voices, and combines those that are within a threshold level of similarity…”) and (Higgins, e.g., [035], “…clustering models, k-Nearest Neighbors (kNN) models…”).
As per as claim 10, the combination Kumar and Chaudhuri disclose:
(Original) The method of claim 1, further comprising determining a performance metric associated with the new candidate group after automatically assigning the one or more records, wherein the performance metric is influenced by one or more performance metrics associated with the one or more records (Kumar, e.g., [0036-0037], “...classify conversations and portions of conversations as being related to one or more of hundreds or thousands of potential categories... conversation mapping engine 113 maps the segments to candidate transactions based on the semantic content and/or metadata associated with the segments...” and [0063-0068], (assigning/label new data to category/groups) and [0080] for updating ).
As per as claim 31, the combination Kumar and Chaudhuri disclose:
(New) The method of claim 1, wherein:
determining the new candidate group comprises automatically discovering the new candidate group distinct from the plurality of existing groups of semantically similar records based on semantic content of the subset of the set of out-of-domain records (Kumar, e.g., [0036-0037], “...classify conversations and portions of conversations as being related to one or more of hundreds or thousands of potential categories... conversation mapping engine 113 maps the segments to candidate transactions based on the semantic content and/or metadata associated with the segments...” and [0063-0068], (assigning/label new data to category/groups) and [0080], [0098-0099], for updating ); and
the subset of the set of out-of-domain records comprises new records more recent than identification of the plurality of existing groups (Kumar, e.g., [0063-0071], “… (e.g., national politics, news stories) may be assigned a label indicating the segment is “not relevant,” “not likely relevant,” “filtered,” etc. It is understood that the name of the label is irrelevant, as long as the result is that the corresponding segment is omitted from mapping to a candidate transaction … a relevancy threshold may be different according to a communication channel over which a conversation is conducted... assign a “not relevant” label to a conversation segment in a text SMS message that includes semantic content associated with a participant’s location …” and [105-0107], (multiple subset)) and see (Chaudhuri, e.g., [0023], (file’s metadata) and [0032-0035], [0042], “…performs voice clustering, i.e., groups detected voices together that are determined to belong to the same speaker…determines which segments of speech in the audio for a video belong to the same speaker... the voice clusterer arrives at a set of candidate voices (i.e., voice clusters) that are most likely to be unique and from separate speakers, although in some cases the voice clusterer may overestimate and determine that there are more candidate voices than actual voices in the video…” and [0113-0114], "the voice detection subsystem determines the set of four different voices A, B, C, and D)..
As per as claim 32, the combination Kumar and Chaudhuri disclose:
(New) The method of claim 31, further comprising receiving, at the database
system, at least a threshold number of the new records after the identification of the plurality of existing groups, wherein determining the new candidate group comprises automatically discovering the new candidate group based on the semantic content of the subset of the set of out-of-domain records in response to receiving at least the threshold number of the new records at the database system (Chaudhuri, e.g., [0034], (multiple threshold value) and [0042] (threshold levels) (unless applicant provide a constitute of each semantical threshold, otherwise base on the claim language the examiner asserts multiple threshold levels can be read as first threshold, second threshold and other threshold) and further see [0066], [0072], [0075], [0094], distance scores (within a threshold value)) and (Kumar, e.g., [0063-0071], [0094-0096])).
As per as claim 33, the combination Kumar and Chaudhuri disclose:
(New) The method of claim 1, further comprising verifying an intra-cluster
distance associated with the new candidate group of semantically similar records is less than a second threshold and an inter-cluster distance between the new candidate group and the plurality of existing groups is greater than a third threshold prior to automatically assigning the one or more records (Chaudhuri, e.g., [0034], (multiple threshold value) and [0042] (threshold levels) (unless applicant provide a constitute of each semantical threshold, otherwise base on the claim language the examiner asserts multiple threshold levels can be read as first threshold, second threshold and other threshold) and further see [0066], [0072], [0075], [0094], distance scores (within a threshold value)). Further see (Kumar, e.g., [0094-0096], “...classify one or more conversation segments... segments failing to meet a threshold level of relevance...”).
As per as claim 34, the combination Kumar and Chaudhuri disclose:
(New) The method of claim 1, wherein the subset of the set of out-of-domain
records comprises a unique subset of the set of out-of-domain unassigned conversations input to an unsupervised model to fit the unsupervised model to the set of out-of-domain unassigned conversations (Kumar, e.g., [0063-0071], “… (e.g., national politics, news stories) may be assigned a label indicating the segment is “not relevant,” “not likely relevant,” “filtered,” etc. It is understood that the name of the label is irrelevant, as long as the result is that the corresponding segment is omitted from mapping to a candidate transaction … a relevancy threshold may be different according to a communication channel over which a conversation is conducted... assign a “not relevant” label to a conversation segment in a text SMS message that includes semantic content associated with a participant’s location …” and [105-0107], (multiple subset)).
Claims 25-27, 29 and 35-36 are rejected under 35 U.S.C. 103 as being unpatentable over Chaudhuri et al. (US PGPUB 2018/0174600, hereinafter Chaudhuri), in view of Kumar et al. (US PGPUB 2023/0252980, hereinafter Kumar) and further in view of Chang et al. (US PGPUB 2021/0149933, hereinafter Chang).
As per as claim 25, the combination Kumar and Chaudhuri disclose:
(Previously Presented) The method of claim 1, further comprising providing a graphical user interface (GUI) display including graphical indicia of the plurality of existing groups and the new candidate group (Chaudhuri, e.g., [0020], “…a user interface provided by the content presenter…presents a user interface with a caption entry field for each timing provided by the content system…” and further see [0031-0034] and [0040-0042], “…detected the existence of faces for each video frame of the video…detect face candidates in the frame, which are potential element in the video that may be a face…”) and (Kumar, e.g., [0092-0094], “... interface platform applies a semantic recognition machine learning model to the conversations stored in the data repository to recognize semantic content in the conversations... interface platform applies one or more conversation classification machine learning models to classify conversation segments based on the semantic content in the conversations. Classifying the conversation segments...”).
To make records clearer regarding to the features of “providing a graphical user interface (GUI) display including graphical indicia of the plurality of group” (although as stated above Chaudhuri functional disclose graphical user interface display of the plurality of group (Chaudhuri, e.g., [0040-0042]).
However Chang, in an analogous art, discloses “providing a graphical user interface (GUI) display including graphical indicia of the plurality of group” (Chang, e.g., fig. 5, associating with texts description, [0075-0077], [0082], “…chat messaging data depicted in the chat messaging GUI display…” and GUI display corresponding to the case database record may be provided within the instance of the virtual application, with the GUI display including a GUI element (e.g., a text box, drop-down menu, radio button, or the like) that corresponds to the particular field being predicted). Thus, it would have been obvious to one of ordinary skill in the art BEFORE the effective filling date of the claimed invention to combine the teaching of Chang, Higgins and Chaudhuri to generating the recommended value based on the combined numerical representation of the conversational data using a prediction model associated with the field, and auto populating the field of the case database object with the recommended value (Chang, e.g., [abstract]).
As pe as claim 26, the combination Chang, Kumar and Chaudhuri disclose:
(Previously Presented) The method of claim 25, further comprising providing a group analysis GUI display comprising a listing of the respective semantic representation of the one or more text fields associated with the respective record of the one or more records in response to user selection of the new candidate group on the GUI display (Chang, e.g., fig. 5, associating with texts description, [0075-0077], [0082], “…chat messaging data depicted in the chat messaging GUI display…” and GUI display corresponding to the case database record may be provided within the instance of the virtual application, with the GUI display including a GUI element (e.g., a text box, drop-down menu, radio button, or the like) that corresponds to the particular field being predicted).
As per as claim 27, the combination Chang, Kumar and Chaudhuri disclose: (Previously Presented) The method of claim 26, wherein:
the plurality of existing groups comprise a plurality of existing contact reason cluster groups (Chaudhuri, e.g., [0018], [001-0034] and [0042], [0085]);
the new candidate group comprises a new contact reason cluster group; and the group analysis GUI display comprises a listing of the representative utterances associated with conversations assigned to the new contact reason cluster group (Chaudhuri, e.g., [0018], [001-0034] and [0042], [0085]) and (Chang, e.g., fig. 5, associating with texts description, [0075-0077], [0082], “…chat messaging data depicted in the chat messaging GUI display…” and GUI display corresponding to the case database record may be provided within the instance of the virtual application, with the GUI display including a GUI element (e.g., a text box, drop-down menu, radio button, or the like) that corresponds to the particular field being predicted) and (Kumar, e.g., [0092-0094], “... interface platform applies a semantic recognition machine learning model to the conversations stored in the data repository to recognize semantic content in the conversations... interface platform applies one or more conversation classification machine learning models to classify conversation segments based on the semantic content in the conversations. Classifying the conversation segments...”).
As per as claim 29, the combination Chang, Kumar and Chaudhuri disclose:
(Previously presented) The method of claim 10, further comprising providing a graphical user interface (GUI) display including graphical indicia of the performance metric associated with the new candidate group and respective performance metrics associated with the plurality of existing groups (Kumar, e.g., [0092-0094], “... interface platform applies a semantic recognition machine learning model to the conversations stored in the data repository to recognize semantic content in the conversations... interface platform applies one or more conversation classification machine learning models to classify conversation segments based on the semantic content in the conversations. Classifying the conversation segments...”).
and (Chaudhuri, e.g., [0020], “…a user interface provided by the content presenter…presents a user interface with a caption entry field for each timing provided by the content system…” and further see [0031-0034] and [0040-0042], “…detected the existence of faces for each video frame of the video…detect face candidates in the frame, which are potential element in the video that may be a face…”).
To make records clearer regarding to the features of “providing a graphical user interface (GUI) display” (although as stated above Chaudhuri functional disclose graphical user interface display (Chaudhuri, e.g., [0040-0042] and (Kumar, e.g., [0092-0094])).
However Chang, in an analogous art, discloses “providing a graphical user interface (GUI) display” (Chang, e.g., fig. 5, associating with texts description, [0075-0077], [0082], “…chat messaging data depicted in the chat messaging GUI display…” and GUI display corresponding to the case database record may be provided within the instance of the virtual application, with the GUI display including a GUI element (e.g., a text box, drop-down menu, radio button, or the like) that corresponds to the particular field being predicted). Thus, it would have been obvious to one of ordinary skill in the art BEFORE the effective filling date of the claimed invention to combine the teaching of Chang, Higgins and Chaudhuri to generating the recommended value based on the combined numerical representation of the conversational data using a prediction model associated with the field, and auto populating the field of the case database object with the recommended value (Chang, e.g., [abstract]).
As per as claim 35, the combination Chang, Kumar and Chaudhuri disclose:
(New) The method of claim 1, further comprising automatically determining,
at the database system, the respective semantic representation of one or more text fields associated with the respective record based on a combination of text values for the one or more text fields of the respective record by inputting the text values for the one or more text fields of the respective record into a summarization model (Chaudhuri, e.g., [0024], [0039-0042], [0051], [0104], “…the text includes a transcription of the speech sound…” and “… text for a caption entry during playback between the start timestamp and the end timestamp of the timing window associated with a caption entry…the text for presentation (e.g., font style, font type, font size, text position on screen, etc.)…” and further (Kumar, e.g., [044], [0076], “...identifies attributes including semantic values and metadata values associated with a conversation segment and a label associated with the segment. The label may include a description of content associated with the conversation segment, such as “meeting re: Product X,” “bid to CEO X for deployment at facility Y,” or “movie watched last night.” According to one or more embodiment, the labels may include values for filtering particular conversation segments...”).
To make records clearer regarding to the features of “combination of text values for the one or more text fields of the respective record by inputting the text values for the one or more text fields of the respective record into a summarization model”.
To make records clearer regarding to the features of “combination of text values for the one or more text fields of the respective record by inputting the text values for the one or more text fields of the respective record into a summarization model”.
However Chang, in an analogous art, discloses “combination of text values for the one or more text fields of the respective record by inputting the text values for the one or more text fields of the respective record into a summarization model” (Chang, e.g., [0075], [0082], [0085], “… chat messaging data and other auxiliary data associated with the case along with one or more fields of the case database record… provide a corresponding graphical representation of the recommended summary notes field value, which in the illustrated embodiment is a text string that reflects the substance of conversation derived or otherwise determined using the chat messaging data…”). Thus, it would have been obvious to one of ordinary skill in the art BEFORE the effective filling date of the claimed invention to combine the teaching of Chang, Kumar and Chaudhuri to generating the recommended value based on the combined numerical representation of the conversational data using a prediction model associated with the field, and auto populating the field of the case database object with the recommended value (Chang, e.g., [abstract]).
As per as claim 36, the combination Chang, Kumar and Chaudhuri disclose:
(New) The method of claim 1, further comprising automatically determining,
at the database system, the respective semantic representation of one or more text fields associated with the respective record based on a combination of text values for the one or more text fields of the respective record by inputting the text values for the one or more text fields of the respective record into an encoder model (Chaudhuri, e.g., [0024], [0039-0042], [0051], [0104], “…the text includes a transcription of the speech sound…” and “… text for a caption entry during playback between the start timestamp and the end timestamp of the timing window associated with a caption entry…the text for presentation (e.g., font style, font type, font size, text position on screen, etc.)…” and further (Kumar, e.g., [044], [0076], “...identifies attributes including semantic values and metadata values associated with a conversation segment and a label associated with the segment. The label may include a description of content associated with the conversation segment, such as “meeting re: Product X,” “bid to CEO X for deployment at facility Y,” or “movie watched last night.” According to one or more embodiment, the labels may include values for filtering particular conversation segments...”).
To make records clearer regarding to the features of “combination of text values for the one or more text fields of the respective record by inputting the text values for the one or more text fields of the respective record into an encoder model”.
However Chang, in an analogous art, discloses “combination of text values for the one or more text fields of the respective record by inputting the text values for the one or more text fields of the respective record into an encoder model” (Chang, e.g., [0075], [0082], [0085], “… chat messaging data and other auxiliary data associated with the case along with one or more fields of the case database record… provide a corresponding graphical representation of the recommended summary notes field value, which in the illustrated embodiment is a text string that reflects the substance of conversation derived or otherwise determined using the chat messaging data…”). Thus, it would have been obvious to one of ordinary skill in the art BEFORE the effective filling date of the claimed invention to combine the teaching of Chang, Kumar and Chaudhuri to generating the recommended value based on the combined numerical representation of the conversational data using a prediction model associated with the field, and auto populating the field of the case database object with the recommended value (Chang, e.g., [abstract]).
Claims 11-13 and 16 are essentially the same as claims 1-3, 8-10 except that they set forth the claimed invention as a non-transitory computer readable storage medium rather a method, respectively and correspondingly, therefore is rejected under the same reasons set forth in rejections of claims 1-3, 8-10.
Claim 19 is essentially the same as claim 1 except that it set forth the claimed invention as a system rather a method, respectively and correspondingly, therefore is rejected under the same reasons set forth in rejections of claim 1.
Response to Arguments
The Examiner respectfully reminds applicant of the broadest reasonable interpretation standard (See MPEP 2111), "During examination, the claims must be interpreted as broadly as their terms reasonably allow." In re American Academy of Science Tech Center, 367 F.3d 1359, 1369, 70 USPQ2d 1827, 1834 (Fed. Cir. 2004) (The USPTO uses a different standard for construing claims than that used by district courts; during examination the USPTO must give claims their broadest reasonable interpretation.) In Phillips v. AWH Corp., 415 F.3d 1303, 75 USPQ2d 1321 (Fed. Cir. 2005), the court further elaborated on the “broadest reasonable interpretation" standard and recognized that “The Patent and Trademark Office (“PTO") determines the scope of claims in patent applications not solely on the basis of the claim language, but upon giving claims their broadest reasonable construction." Thus, when interpreting claims, the courts have held that Examiners should (1) interpret claim terms as broadly as their terms reasonably allows and (2) interpret claim phrases as broadly as their construction reasonably allows.
Applicant’s arguments filed 10/14/2025 with respect to claims 1-3, 8, 10-13, 16, 19, 25-27, 29 and 31-36 have been considered but are moot in view of the new ground(s) of rejection necessitated by applicant's amendment to the claims. Applicant's newly amended features are taught implicitly, expressly, or impliedly by the prior art of record (See the new ground(s) of rejection set forth herein above).
Issue I: Applicant argued on pages 9-10 (Remarks/Argument) regarding claims 1-3, 8, 10-13, 16, 19, 25-27, 29 and 31-36 rejection under 35 U.S.C. 101.
Response I: After review and consideration, the examiner respectfully submits the claims 1, 11 and 19 recites the steps of “identifying a set of records...”, “determining a new candidate group...”, “assigning records to the new candidate group...” and “updating records...to the new candidate group”, therefore these steps falls within the “Human Activity” grouping of abstract ideas. Accordingly, the claim recites an abstract idea.
This judicial exception is not integrated into a practical application. In particular, the claim only recites one additional element – using a processor or non-transitory machine readable storage medium to perform the identifying, determining, assigning, updating steps (Note that, the steps of identifying, determining, assigning, updating can be interprets as gathering information which is abstract ideas falls within the “Human Activity” as stated above”). The processor and machine readable storage medium in those steps is recited at a high-level of generality (i.e., as a generic processor performing a generic computer function of identifying and updating records) such that it amounts no more than mere instructions to apply the exception using a generic computer component. Accordingly, this additional element does not integrate the abstract idea into a practical application because it does not integrate the abstract idea into a practical application because it does not impose any meaningful limits on practicing the abstract idea. The claim is directed to an abstract idea.
The claim does not include additional elements that are sufficient to amount to significantly more than the judicial exception. As discussed above with respect to integration of the abstract idea into a practical application, the additional element of using a processor the identifying, determining, assigning, updating steps amounts to no more than mere instructions to apply the exception using a generic computer component. Mere instructions to apply an exception using a generic computer component cannot provide an inventive concept. The claim is not patent eligible.
Issue II: Regarding to 103 rejection.
Response II: See the above new ground rejection.
The Examiner respectfully submits that, with respect to the totally newly amended subject matter, the Examiner respectfully cited proper paragraphs from cited reference to reject the claim in responsive to the newly amended, please refer to the corresponding section of the office action.
Additional Art Considered
The prior art made of record and not relied upon is considered pertinent to the Applicants’ disclosure.
The following patents and papers are cited to further show the state of the art at the time of Applicants’ invention with respect to assigning structural metadata involves determining a candidate group of semantically similar conversations based on unassigned conversations, determining a clustering performance metric associated with the candidate group based on a relationship between the candidate group and a plurality of existing groups of semantically similar conversations, and when the clustering performance metric is greater than a threshold, automatically assigning one or more unassigned conversations to the candidate group based on the representative utterances associated therewith.
a. Konam et al. (US PGPUB 2023/0223016, hereafter Konam); “User Interface Linking Analyzed Segments Of Transcripts With Extracted Key Points” discloses “user interface (UI) linking analyzed segments of transcripts with extracted key points may be provided by capturing audio of a conversation including first and second pluralities of utterances respectively spoken by first and second parties; transmitting the audio to a Natural Language Processing (NLP) system; receiving a transcript of the conversation and analysis outputs from the transcript including a key point and hyperlink to a most-semantically-relevant segment of a plurality of segments included in the transcript for the key point according to a semantic context for the key point within the conversation; displaying, in a UI, the transcript and a selectable representation of the key point”.
Konam also teaches selected semantic category of a plurality of semantic categories identified from the conversation (Konam, e.g., [006-007]).
Konam further teaches set of classified conversation to categories [0090], analyze various candidate categories to group the key points into, and scores each key point in a vector space with various features related to each candidate category. When a key point has a relevancy score above a relevancy threshold [0119-0121].
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 extension fee 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 date of this final action.
Any inquiry concerning this communication or earlier communications from the examiner should be directed to TUAN A PHAM whose telephone number is (571)270-3173. The examiner can normally be reached M-F 7:45 AM - 6:30 PM.
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/TUAN A PHAM/Primary Examiner, Art Unit 2163
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