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 .
DETAILED ACTION
This office action is in response to application 18/977,366, which was filed 12/11/24 and is a continuation of application 18/922,970, currently pending, which is a continuation of application 17/305,976, now U.S. Patent No. 12,158,902. Claims 1-20 are pending in the application and have been considered.
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 obviousness-type 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); and 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 a nonstatutory double patenting ground provided the conflicting application or patent either is shown to be commonly owned with this application, or claims an invention made as a result of activities undertaken within the scope of a joint research agreement.
Effective January 1, 1994, a registered attorney or agent of record may sign a terminal disclaimer. A terminal disclaimer signed by the assignee must fully comply with 37 CFR 3.73(b).
Claims 1-20 are provisionally rejected on the ground of nonstatutory obviousness-type double patenting as being unpatentable over claims 1-20 of copending application 18/922,970.
Claims 1-5, 8-12, and 15-19 are rejected on the ground of nonstatutory obviousness-type double patenting as being unpatentable over claims 1-4, 10-12, 14, and 15 of US Patent 12,158,902.
Specifically, a comparison of claim 1 in the present application with claim 1 of copending application 18/922,970 and claim 1 of US Patent 12,158,902 yields the following:
(Present application) (application 18/922,970) (US Patent 12,158,902) (Examiner Notes)
1. … the trained ML model identifying for each transcript portion a corresponding conjunction of a corresponding portion topic detailed by a corresponding portion parameter and by a corresponding portion outcome
1. A method comprising: identifying, by a machine-learning (ML) model, portion topics, corresponding portion parameters, and corresponding portion outcomes… each of multiple transcript portions has a corresponding portion topic detailed by a corresponding portion parameter and by a corresponding portion outcome,
1. A method comprising: identifying, by a machine-learning (ML) model, portion topics with corresponding portion parameters and corresponding portion outcomes …each transcript portion among the multiple transcript portions having a corresponding portion topic that is detailed by a corresponding portion parameter and detailed by a corresponding portion outcome
None
providing, to the trained ML model, one or more transcripts that each include multiple transcript portions
of transcript portions in one or more transcripts within which
in one or more transcripts, … transcript data that includes text of multiple transcript portions from the one or more transcripts;
None
training a machine-learning (ML) model based on training conjunctions of training topics, corresponding training parameters, and corresponding training outcomes
the ML model being trained based on training topics, training parameters, and training outcomes;
the ML model having been trained with data from a training set that comprises training transcripts with identified portion topics with corresponding portion parameters and corresponding portion outcomes within the training transcripts
“conjunctions” of training topics, training parameters, and training outcomes is implicit since these are joined
performing a search of the one or more transcripts based on a specified conjunction of a specified portion topic with at least one of a specified portion parameter or a specified portion outcome; and
performing ….a search of the multiple transcript portions… based on a specified conjunction of a specified portion topic with at least one of a specified portion parameter or a specified portion outcome; and
performing, by the one or more processors, a search for one or more transcript portions among the multiple transcript portions based on the specified conjunction of the specified portion topic with at least one of the specified portion parameter or the specified portion outcome; and
None
causing presentation of results from the performed search.
causing presentation of results from the performed search.
causing presentation of results from the performed search within the UI.
None
As the table above demonstrates, although the language is not identical, each limitation of claim 1 of the present application is found in claim 1 of copending application 18/922,970 as well as claim 1 of US Patent 12,158,902, and therefore, the claim is anticipated. Independent claims 8 and 15 are similarly anticipated by claims 8 and 15 of copending application 18/922,970 and claims 10 and 14 of US Patent 12,158,902 respectively. Dependent claims 2-7, 9-14, and 16-20 of the present application are directed to the same subject matter as dependent claims 2-7, 9-14, and 16-20 of copending application 18/922,970, and therefore are also anticipated. Dependent claims 2-5 of the present application are substantially similar to the subject matter of claims 1-4 of US Patent 12,158,902 respectively, and therefore are also anticipated, as shown below. Dependent claims 9-12 and 16-19 contain similar subject matter to that in claims 2-5 discussed above, and are anticipated by claims 1-4, 1-12, 14, and 15 for reasons similar to those for claims 2-5 as shown below.(Present application) (US Patent 12,158,902) (Examiner Notes)
2. The method of claim 1, further comprising: providing a user interface (UI) operable to specify the search of the one or more transcripts based on the specified conjunction of the specified portion topic with at least one of the specified portion parameter or the specified portion outcome; and the results from the performed search are presented by the provided UI.
1. ….
providing a user interface (UI) for searching the transcript data based on a specified conjunction of a specified portion topic with at least one of a specified portion parameter or a specified portion outcome; performing a search for one or more transcript portions among the multiple transcript portions based on the specified conjunction of the specified portion topic with at least one of the specified portion parameter or the specified portion outcome; and
causing presentation of results from the performed search within the UI.
The search is based on a “specified conjunction”, and the UI is for searching, which amounts to providing a user interface operable to specify the search.
3. The method of claim 1, wherein: features of the ML model include one or more turns within the one or more transcripts; and the method further comprises: identifying one or more turns within the one or more transcripts.
2. The method of claim 1, wherein features of the ML model include one or more turns within the one or more transcripts.
5. The method of claim 1, further comprising:
identifying turns within the transcript data.
None
4. The method of claim 1, wherein: the performing of the search based on the specified conjunction of the specified portion topic with at least one of the specified portion parameter or the specified portion outcome includes: converting a single search query into multiple search queries; and combining results of the multiple search queries into a single output.
3. The method of claim 1, wherein performing the search further comprises:
converting a single search query into multiple searches; and
combining results of the multiple searches into one output.
See above claim 2 notes.
5. The method of claim 1, wherein: the search of the one or more transcripts based on the specified conjunction of the specified portion topic with at least one of the specified portion parameter or the specified portion outcome is specified based on a value of a tag, and the performing of the search is based on the value of the tag.
1. performing, by the one or more processors, a search for one or more transcript portions among the multiple transcript portions based on the specified conjunction of the specified portion topic with at least one of the specified portion parameter or the specified portion outcome
4. The method of claim 1, wherein the UI includes a search option operable to specify a first value for a first state tag, wherein performing the search further comprises:
identifying one or more transcript portions that include the first value for the first state tag.
None
Unlike the rejections based on the claims of US Patent 12,158,902, the rejection based on the claims of copending application 18/922,970 is a provisional nonstatutory double patenting rejection because the patentably indistinct claims have not in fact been patented.
Claim Rejections - 35 USC § 101
35 U.S.C. 101 reads as follows:
Whoever invents or discovers any new and useful process, machine, manufacture, or composition of matter, or any new and useful improvement thereof, may obtain a patent therefor, subject to the conditions and requirements of this title.
Claims 1-20 are rejected under 35 U.S.C. 101 because the claimed invention is directed to non-statutory subject matter.
Claim 1 is rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more. The claim recites “training a machine-learning (ML) model based on training conjunctions of training topics, corresponding training parameters, and corresponding training outcomes; providing, to the trained ML model, one or more transcripts that each include multiple transcript portions, the trained ML model identifying for each transcript portion a corresponding conjunction of a corresponding portion topic detailed by a corresponding portion parameter and by a corresponding portion outcome; performing a search of the one or more transcripts based on a specified conjunction of a specified portion topic with at least one of a specified portion parameter or a specified portion outcome; and causing presentation of results from the performed search”
The limitation of training a machine-learning (ML) model based on training conjunctions of training topics, corresponding training parameters, and corresponding training outcomes, as drafted, is a process that, under its broadest reasonable interpretation, is not considered to render the claim eligible because the computing elements in this step are recited at a high-level of generality (i.e., general purpose training of a generic machine learning model generic training topics, training parameters, and training outcomes) such that they amount to no more than mere instructions to apply the exception using generic computer elements. For example, as those skilled in the art would be familiar, machine learning models were known to have training topics (i.e. topics found in the training example data set), training parameters (the parameters which are trained) and training outcomes (i.e. what was the model supposed to predict given an input). Accordingly, these additional elements do not integrate the abstract idea into a practical application because they do not impose any meaningful limits on practicing the abstract idea.
The limitation of “providing, to the trained ML model, one or more transcripts that each include multiple transcript portions, the trained ML model identifying for each transcript portion a corresponding conjunction of a corresponding portion topic detailed by a corresponding portion parameter and by a corresponding portion outcome”, as drafted, is a process that, under its broadest reasonable interpretation, but for “the trained ML model”, covers performance of the limitation in the mind. For example, “providing, …, one or more transcripts that each include multiple transcript portions, … identifying for each transcript portion a corresponding conjunction of a corresponding portion topic detailed by a corresponding portion parameter and by a corresponding portion outcome” in the context of this claim encompasses providing one or more paper transcripts that each include multiple transcript portions, and mentally identifying for each transcript portion a corresponding conjunction of a corresponding portion topic detailed by a corresponding portion parameter and by a corresponding portion outcome. The “trained ML model” does not integrate the abstract idea into a practical application for similar reasons to those explained above.
The limitation of “performing a search of the one or more transcripts based on a specified conjunction of a specified portion topic with at least one of a specified portion parameter or a specified portion outcome”, as drafted, is a process that, under its broadest reasonable interpretation, covers performance of the limitation in the mind. For example, “performing a search of the one or more transcripts based on a specified conjunction of a specified portion topic with at least one of a specified portion parameter or a specified portion outcome” in the context of this claim encompasses reading a specified conjunction of a specified portion topic with at least one of a specified portion parameter or a specified portion outcome and visually scanning the transcript with the conjunction in mind.
Similarly, the limitation of “causing presentation of results from the performed search”, as drafted, is a process that, under its broadest reasonable interpretation, covers performance of the limitation in the mind. For example, “causing presentation of results from the performed search” in the context of this claim encompasses writing down the results from the search on a sheet of paper and holding it up for display.
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 “Mental Processes” 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 additional elements of – “by a machine-learning (ML) model…, the ML model being trained based on training topics, training parameters, and training outcomes”. The computing elements in this step are recited at a high-level of generality (i.e., as a generic machine learning model trained on generic training topics, training parameters, and training outcomes) such that they amount to no more than mere instructions to apply the exception using generic computer elements. For example, as those skilled in the art would be familiar, machine learning models were known to have training topics (i.e. topics found in the training example data set), training parameters (the parameters which are trained) and training outcomes (i.e. what was the model supposed to predict given an input). Accordingly, these additional elements do not integrate the abstract idea into a practical application because they do not impose any meaningful limits on practicing the abstract idea. It is further noted that the “performing a search…” step does not even use the machine learning identified portions topics, portions parameters, and portion outcomes but merely “a specified portion topic with at least one of a specified portion parameter or a specified portion output”, which further weighs against eligibility of the claim. The claim does not include additional elements that are sufficient to amount to significantly more than the judicial exception for at least these reasons. As discussed above with respect to integration of the abstract idea into a practical application, the additional element of using machine learning to perform the identifying 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.
Specifically with respect to Step 2A, Prong Two, of the Alice/Mayo test, the judicial exception is not integrated into a practical application. Claim 1 does not recite any limitations that are not mental steps.
Specifically with respect to Step 2B of the Alice/Mayo test, “the claim as a whole does not amount to significantly more than the exception itself (there is no inventive concept in the claim)”. MPEP 2106.05 Il. There are no limitations in claim 1 outside of the judicial exception. As a whole, there does not appear to contain any inventive concept. As discussed above, claim 1 is a mental process that pertains to the mental process of identifying transcript portions and searching a transcript, which can be performed entirely by a human with physical aids.
Dependent claims 2-7 depend from claim 1, do not remedy any of the deficiencies of claim 1, and therefore are rejected on the same grounds as claim 1 above.
Generally, claims 2-7 merely recite additional steps for identifying transcript portions, searching a transcript, and presenting results, all of which could be performed mentally or by writing down relationships with a pen and paper, and do not amount to anything more than substantially the same abstract idea as explained with respect to claim 1.
Specifically:
Claim 2 recites “providing a user interface (UI) operable to specify the search of the one or more transcripts based on the specified conjunction of the specified portion topic with at least one of the specified portion parameter or the specified portion outcome; and the results from the performed search are presented by the provided UI” which could be performed by presenting a user with a paper form on which to specify a search, with a results field that is filled out after the search and presented back to the user.
Claim 3 recites “features of the ML model include one or more turns within the one or more transcripts; and the method further comprises: identifying one or more turns within the one or more transcripts” which could be performed by mentally identifying one or more turns within the one or more transcripts. Further specifying that “features of the ML model include one or more turns within the one or more transcripts” merely specifies which features the ML model accepts to include turns within the transcripts as features, which is insufficient to integrate the abstract idea into a practical application because it not impose any meaningful limits on the ML model itself that would impose any meaningful limits on practicing the abstract idea.
Claim 4 recites “the performing of the search based on the specified conjunction of the specified portion topic with at least one of the specified portion parameter or the specified portion outcome includes: converting a single search query into multiple search queries; and combining results of the multiple search queries into a single output” which could be performed by mentally converting a single search query into multiple search queries; and on a sheet of paper, combining results of the multiple search queries into a single output.
Claim 5 recites “the search of the one or more transcripts based on the specified conjunction of the specified portion topic with at least one of the specified portion parameter or the specified portion outcome is specified based on a value of a tag, and the performing of the search is based on the value of the tag” which could be performed by reading the value of a specified tag, and visually performing the search based on the value of the specified tag.
Claim 6 recites “the search of the one or more transcripts based on the specified conjunction of the specified portion topic with at least one of the specified portion parameter or the specified portion outcome is specified based on a first value of a first tag conjoined by an AND operator with a second value of a second tag, and the performing of the search is based on the first value of the first tag conjoined by the AND operator with the second value of the second tag” which could be performed by reading the values of a first and second tag and visually looking in the transcript for text that matches both tags.
Claim 7 recites “the search of the one or more transcripts based on the specified conjunction of the specified portion topic with at least one of the specified portion parameter or the specified portion outcome is specified based on a first value of a first tag conjoined by an OR operator with a second value of a second tag, and the performing of the search is based on the first value of the first tag conjoined by the OR operator with the second value of the second tag” which could be performed by reading the values of a first and second tag and visually looking in the transcript for text that matches either tag.
In sum, claims 2-7 depend from claim 1 and further recite mental processes as explained above. None of the additional limitations recited in claims 2-7 amount to anything more than the same or a similar abstract idea as recited in claim 1. Nor do any limitations in claims 2-7 (a) integrate the abstract idea into a practical application because they do not impose any meaningful limits on practicing the abstract idea or (b) amount to significantly more than the judicial exception. Claims 2-7 are not patent eligible.
Claim 8 is directed to a system that corresponds to the method of claim 1 and is therefore rejected for the same reasons set for the above with respect to claim 1. While claim 8 recites generic computer components (one or more processors, one or more memories storing instructions), such generic computing components are recited at a high-level of generality (i.e., as a generic processor and memory performing generic computer functions) such that they amount to no more than mere instructions to apply the exception using generic computer components. Accordingly, these additional elements do not integrate the abstract idea into a practical application because they do not impose any meaningful limits on practicing the abstract idea. The claim is directed to an abstract idea.
Claim 8 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 limitations of using generic computer components amount to no more than mere instructions to apply the exception using generic computer components. Mere instructions to apply an exception using generic computer components cannot provide an inventive concept. Claim 8 is not patent eligible.
Claims 9-14 depend from claim 8, do not remedy any of the deficiencies of claim 8, and correspond to the subject matter discussed above with regard to dependent claims 2-7 respectively, therefore are rejected on the same grounds as claim 2-8 above.
Claim 15 is directed to a non-transitory computer readable storage medium that corresponds to the system of claim 8 and is therefore rejected for the same reasons set forth above with respect to claim 8. Moreover, while claim 8 recites generic computing components (e.g., instructions, processor), such components are only claimed at a high-level of generality and are not sufficient to render the claim subject matter eligible for the same reasons discussed above with respect to claims 1 and 8.
Claims 16-20 depend from claim 15, do not remedy any of the deficiencies of claim 15, and correspond to the subject matter discussed above with regard to dependent claims 2-6 respectively, therefore are rejected on the same grounds as claim 2-6 and 15 above.
Claim Rejections - 35 USC § 102
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.
The following is a quotation of the appropriate paragraphs of 35 U.S.C. 102 that form the basis for the rejections under this section made in this Office action:
A person shall be entitled to a patent unless –
(a)(1) the claimed invention was patented, described in a printed publication, or in public use, on sale or otherwise available to the public before the effective filing date of the claimed invention.
Claims 1-3, 5, 6, 8-10, 12, 13, 15-17, 19, and 20 are rejected under 35 U.S.C. 102(a)(1) as being anticipated by Meteer et al. (US 20190180175).
Consider claim 1, Meteer discloses a method (method for automating segmentation and annotation of targeted portions of electronic communications, [0001]) comprising:
training a machine-learning (ML) model based on training conjunctions of training topics, corresponding training parameters, and corresponding training outcomes (training a ML model with a training set of manually labeled clusters of segments based on semantic similarity, the clustered segments having topic, parameter, and outcome labels, [0078-0081], [0106]);
providing, to the trained ML model, one or more transcripts that each include multiple transcript portions, the trained ML model identifying for each transcript portion a corresponding conjunction of a corresponding portion topic detailed by a corresponding portion parameter and by a corresponding portion outcome (classifier identifies waypoints within segments, Fig 4, [0085], the waypoints having topic labels with parameters, Table 1, [0063], and outcomes for resolution waypoints, [0099], [0106]);
performing a search of the one or more transcripts based on a specified conjunction of a specified portion topic with at least one of a specified portion parameter or a specified portion outcome (administrator can perform a keyword search and receive results filtered by outcome of the communications, reason for the communications, customer name, etc., [0099], Fig. 7; this is considered a conjunction of a keyword topic and parameter or outcome filter); and
causing presentation of results from the performed search (administrator views results of the search on UI in Fig. 7, [0099], Fig. 7).
Consider claim 8, Meteer discloses a system (system for automating segmentation and annotation of targeted portions of electronic communications, [0001]) comprising: one or more processors (processor, [0036]); and one or more memories storing instructions that, when executed by the one or more processors (memory with instructions executed by processor, [0036]), cause the system to perform operations comprising:
training a machine-learning (ML) model based on training conjunctions of training topics, corresponding training parameters, and corresponding training outcomes (training a ML model with a training set of manually labeled clusters of segments based on semantic similarity, the clustered segments having topic, parameter, and outcome labels, [0078-0081], [0106]);
providing, to the trained ML model, one or more transcripts that each include multiple transcript portions, the trained ML model identifying for each transcript portion a corresponding conjunction of a corresponding portion topic detailed by a corresponding portion parameter and by a corresponding portion outcome (classifier identifies waypoints within segments, Fig 4, [0085], the waypoints having topic labels with parameters, Table 1, [0063], and outcomes for resolution waypoints, [0099], [0106]);
performing a search of the one or more transcripts based on a specified conjunction of a specified portion topic with at least one of a specified portion parameter or a specified portion outcome (administrator can perform a keyword search and receive results filtered by outcome of the communications, reason for the communications, customer name, etc., [0099], Fig. 7; this is considered a conjunction of a keyword topic and parameter or outcome filter); and
causing presentation of results from the performed search (administrator views results of the search on UI in Fig. 7, [0099], Fig. 7).
Consider claim 15, Meteer discloses a non-transitory machine-readable medium storing instructions that, when executed by one or more processors, cause the one or more processors to perform operations (memory with instructions executed by processor, [0036]) comprising:
training a machine-learning (ML) model based on training conjunctions of training topics, corresponding training parameters, and corresponding training outcomes (training a ML model with a training set of manually labeled clusters of segments based on semantic similarity, the clustered segments having topic, parameter, and outcome labels, [0078-0081], [0106]);
providing, to the trained ML model, one or more transcripts that each include multiple transcript portions, the trained ML model identifying for each transcript portion a corresponding conjunction of a corresponding portion topic detailed by a corresponding portion parameter and by a corresponding portion outcome (classifier identifies waypoints within segments, Fig 4, [0085], the waypoints having topic labels with parameters, Table 1, [0063], and outcomes for resolution waypoints, [0099], [0106]);
performing a search of the one or more transcripts based on a specified conjunction of a specified portion topic with at least one of a specified portion parameter or a specified portion outcome (administrator can perform a keyword search and receive results filtered by outcome of the communications, reason for the communications, customer name, etc., [0099], Fig. 7; this is considered a conjunction of a keyword topic and parameter or outcome filter); and
causing presentation of results from the performed search (administrator views results of the search on UI in Fig. 7, [0099], Fig. 7).
Consider claim 2, Meteer discloses: providing a user interface (UI) operable to specify the search of the one or more transcripts based on the specified conjunction of the specified portion topic with at least one of the specified portion parameter or the specified portion outcome (the UI in Fig 7, [0099]); and the results from the performed search are presented by the provided UI (presenting results on the UI, Fig. 7, [0099]).
Consider claim 3, Meteer discloses: features of the ML model include one or more turns within the one or more transcripts (portions of the transcript that machine learning classifier identifies as a reason request waypoint, [0086], Table 3); and the method further comprises: identifying one or more turns within the one or more transcripts (by communication and section ID, [0086], Table 3).
Consider claim 5, Meteer discloses: the search of the one or more transcripts based on the specified conjunction of the specified portion topic with at least one of the specified portion parameter or the specified portion outcome is specified based on a value of a tag, and the performing of the search is based on the value of the tag (performing a keyword search filtered by e.g. date of call, considered a conjunction of the keyword topic with the value for date of the call, Fig. 7, [0099]).
Consider claim 6, Meteer discloses: the search of the one or more transcripts based on the specified conjunction of the specified portion topic with at least one of the specified portion parameter or the specified portion outcome is specified based on a first value of a first tag conjoined by an AND operator with a second value of a second tag, and the performing of the search is based on the first value of the first tag conjoined by the AND operator with the second value of the second tag (the UI in Fig. 7 instructs the administer to “SELECT FILTERS:” (plural), which means the filters are applied together with an implicit logical “AND”, such that the administrator can filter by e.g. DATE OF CALL and HOUR OF CALL, Fig. 7, [0099]).
Consider claim 9, Meteer discloses: providing a user interface (UI) operable to specify the search of the one or more transcripts based on the specified conjunction of the specified portion topic with at least one of the specified portion parameter or the specified portion outcome (the UI in Fig 7, [0099]); and the results from the performed search are presented by the provided UI (presenting results on the UI, Fig. 7, [0099]).
Consider claim 10, Meteer discloses: features of the ML model include one or more turns within the one or more transcripts (portions of the transcript that machine learning classifier identifies as a reason request waypoint, [0086], Table 3); and the method further comprises: identifying one or more turns within the one or more transcripts (by communication and section ID, [0086], Table 3).
Consider claim 12, Meteer discloses: the search of the one or more transcripts based on the specified conjunction of the specified portion topic with at least one of the specified portion parameter or the specified portion outcome is specified based on a value of a tag, and the performing of the search is based on the value of the tag (performing a keyword search filtered by e.g. date of call, considered a conjunction of the keyword topic with the value for date of the call, Fig. 7, [0099]).
Consider claim 13, Meteer discloses: the search of the one or more transcripts based on the specified conjunction of the specified portion topic with at least one of the specified portion parameter or the specified portion outcome is specified based on a first value of a first tag conjoined by an AND operator with a second value of a second tag, and the performing of the search is based on the first value of the first tag conjoined by the AND operator with the second value of the second tag (the UI in Fig. 7 instructs the administer to “SELECT FILTERS:” (plural), which means the filters are applied together with an implicit logical “AND”, such that the administrator can filter by e.g. DATE OF CALL and HOUR OF CALL, Fig. 7, [0099]).
Consider claim 16, Meteer discloses: providing a user interface (UI) operable to specify the search of the one or more transcripts based on the specified conjunction of the specified portion topic with at least one of the specified portion parameter or the specified portion outcome (the UI in Fig 7, [0099]); and the results from the performed search are presented by the provided UI (presenting results on the UI, Fig. 7, [0099]).
Consider claim 17, Meteer discloses: features of the ML model include one or more turns within the one or more transcripts (portions of the transcript that machine learning classifier identifies as a reason request waypoint, [0086], Table 3); and the method further comprises: identifying one or more turns within the one or more transcripts (by communication and section ID, [0086], Table 3).
Consider claim 19, Meteer discloses: the search of the one or more transcripts based on the specified conjunction of the specified portion topic with at least one of the specified portion parameter or the specified portion outcome is specified based on a value of a tag, and the performing of the search is based on the value of the tag (performing a keyword search filtered by e.g. date of call, considered a conjunction of the keyword topic with the value for date of the call, Fig. 7, [0099]).
Consider claim 20, Meteer discloses: the search of the one or more transcripts based on the specified conjunction of the specified portion topic with at least one of the specified portion parameter or the specified portion outcome is specified based on a first value of a first tag conjoined by an AND operator with a second value of a second tag, and the performing of the search is based on the first value of the first tag conjoined by the AND operator with the second value of the second tag (the UI in Fig. 7 instructs the administer to “SELECT FILTERS:” (plural), which means the filters are applied together with an implicit logical “AND”, such that the administrator can filter by e.g. DATE OF CALL and HOUR OF CALL, Fig. 7, [0099]).
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 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 of this title, if the differences between the claimed invention and the prior art are such that the claimed invention as a whole would have been obvious before the effective filing date of the claimed invention to a person having ordinary skill in the art to which the claimed invention pertains. Patentability shall not be negated by the manner in which the invention was made.
Claims 4, 7, 11, 14, and 18 are rejected under 35 U.S.C. 103 as being unpatentable over Meteer et al. (US 20190180175) in view of Wasserblat et al. (US 20150032448).
Consider claim 4, Meteer discloses performing of the search based on the specified conjunction of the specified portion topic with at least one of the specified portion parameter or the specified portion outcome (administrator can perform a keyword search and receive results filtered by outcome of the communications, reason for the communications, customer name, etc., [0099], Fig. 7).
Meteer does not specifically mention converting a single search query into multiple search queries; and combining results of the multiple search queries into a single output.
Wasserblat discloses converting a single search query into multiple search queries (search term expansion is performed by adding term expansion words to the search query by using logical operators such as OR operators, [0055]; this is logically considered to result “multiple search queries”); and combining results of the multiple search queries into a single output (the output of the enhanced search query is retrieved textual transcripts which are presented to the user, [0025]).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify the invention of Meteer by converting a single search query into multiple search queries; and combining results of the multiple search queries into a single output in order to improve precision and recall of the retrieval of textual transcripts, as suggested by Wasserblat ([0043]). Doing so would have led to predictable results of improved search quality as suggested by Wasserblat ([0043]). The references cited are analogous art in the same field of speech processing.
Consider claim 7, Meteer discloses: the search of the one or more transcripts based on the specified conjunction of the specified portion topic with at least one of the specified portion parameter or the specified portion outcome is specified based on a first value of a first tag with a second value of a second tag, and the performing of the search is based on the first value of the first tag with the second value of the second tag (performing a keyword search filtered by e.g. date of call, hour of call, etc. considered a conjunction of the keyword topic with the values, i.e. tags, for date, hour, etc., Fig. 7, [0099]).
Meteer does not specifically mention a first value conjoined by an OR operator with a second value.
Wasserblat discloses a first value conjoined by an OR operator with a second value (query terms conjoined with a logical OR operator, [0055]).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify the invention of Meteer by including a first value conjoined by an OR operator with a second value for reasons similar to those for claim 4.
Consider claim 11, Meteer discloses performing of the search based on the specified conjunction of the specified portion topic with at least one of the specified portion parameter or the specified portion outcome (administrator can perform a keyword search and receive results filtered by outcome of the communications, reason for the communications, customer name, etc., [0099], Fig. 7).
Meteer does not specifically mention converting a single search query into multiple search queries; and combining results of the multiple search queries into a single output.
Wasserblat discloses converting a single search query into multiple search queries (search term expansion is performed by adding term expansion words to the search query by using logical operators such as OR operators, [0055]; this is logically considered to result “multiple search queries”); and combining results of the multiple search queries into a single output (the output of the enhanced search query is retrieved textual transcripts which are presented to the user, [0025]).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify the invention of Meteer by converting a single search query into multiple search queries; and combining results of the multiple search queries into a single output for reasons similar to those for claim 4.
Consider claim 14, Meteer discloses: the search of the one or more transcripts based on the specified conjunction of the specified portion topic with at least one of the specified portion parameter or the specified portion outcome is specified based on a first value of a first tag with a second value of a second tag, and the performing of the search is based on the first value of the first tag with the second value of the second tag (performing a keyword search filtered by e.g. date of call, hour of call, etc. considered a conjunction of the keyword topic with the values, i.e. tags, for date, hour, etc., Fig. 7, [0099]).
Meteer does not specifically mention a first value conjoined by an OR operator with a second value.
Wasserblat discloses a first value conjoined by an OR operator with a second value (query terms conjoined with a logical OR operator, [0055]).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify the invention of Meteer by including a first value conjoined by an OR operator with a second value for reasons similar to those for claim 4.
Consider claim 18, Meteer discloses performing of the search based on the specified conjunction of the specified portion topic with at least one of the specified portion parameter or the specified portion outcome (administrator can perform a keyword search and receive results filtered by outcome of the communications, reason for the communications, customer name, etc., [0099], Fig. 7).
Meteer does not specifically mention converting a single search query into multiple search queries; and combining results of the multiple search queries into a single output.
Wasserblat discloses converting a single search query into multiple search queries (search term expansion is performed by adding term expansion words to the search query by using logical operators such as OR operators, [0055]; this is logically considered to result “multiple search queries”); and combining results of the multiple search queries into a single output (the output of the enhanced search query is retrieved textual transcripts which are presented to the user, [0025]).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify the invention of Meteer by converting a single search query into multiple search queries; and combining results of the multiple search queries into a single output for reasons similar to those for claim 4.
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure.
US 20100104086 Park discloses automatic call segmentation and labeling of conversation transcripts from a call center
US 11055649 Sekar discloses analyzing transcripts between a customer and agent at a call center
US 11087094 Chatterjee discloses analyzing transcripts to generate conversation graphs
US 20140297268 Govrin discloses a topic based system for automated context aware dialog with human users
US 11005786 Zhang discloses a knowledge-driven dialog support conversation system
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/Jesse S Pullias/
Primary Examiner, Art Unit 2655 08/11/26
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