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 correspondence 08/07/26 regarding application 18/922,970, in which claims 1, 2, 5-9, 12-16, 19, and 20 were amended. Claims 1-20 are pending in the application and have been considered.
Response to Arguments
The proper terminal disclaimers filed 08/07/26 disclaiming the terminal part of the statutory term of any patent granted on the instant application which would extend beyond the expiration date of the full statutory term of prior patents 12,158,902 and any patent granted on pending Application 18/977,366 overcome the nonstatutory obviousness type double patenting rejections, and so the rejections are withdrawn.
On page 9, regarding the 35 U.S.C. 101 rejections, Applicant argues that the claimed “…identifying, by a machine-learning (ML) model, portion topics, corresponding portion parameters, and corresponding portion outcomes of transcript portions in one or more transcripts within which each of multiple transcript portions has a corresponding portion topic detailed by a corresponding portion parameter and by a corresponding portion outcome, the ML model being trained based on training topics, training parameters, and training outcomes…” is not practically performable in the human mind because each one of the multiple portions must be kept correlated with its identified corresponding portion topics, identified corresponding position parameters, and identified corresponding portion outcomes, and that for practical purposes in processing “one or more transcripts” a human mind is insufficient, and the recited “machine learning (ML) model” is required to perform this identifying of so many corresponding things.
In response, the examiner is not convinced. As Applicant points out, the claim only requires processing “one or more transcripts” which could be as little as a single transcript. One could read a paper transcript of a call with a greeting, a reason for calling, an offer of assistance, and a conclusion, for example, and mentally identify each of these transcripts portions by their corresponding topic. One could further mentally identify parameters such as the name of the person greeted, the particular reason for calling, the particular offer of assistance, and particular phrase used for the conclusion (e.g. goodbye). One could further mentally identify portion outcomes such as successful greeting, successful identification of reason for calling, successful offer of assistance, and successful conclusion. Applicant seems to argue that keeping the correlation between the portions and their identified corresponding portion topics, identified corresponding position parameters, and identified corresponding portion outcomes would be too mentally difficult, but the above example only requires correlating twelve concepts, and further, according to MPEP 2016.04(a)(2), “The use of a physical aid (e.g., pencil and paper or a slide rule) to help perform a mental step (e.g., a mathematical calculation) does not negate the mental nature of the limitation, but simply accounts for variations in memory capacity from one person to another”. If one had trouble remembering the correlation between the twelve concepts, one could always annotate the transcript with a pen. No machine learning model would be required.
On pages 10-11 Applicant further argues, regarding the 35 U.S.C. 101 rejections, that the claims amount to a practical application that improves the technical field of transcript search, allegedly because only identified transcript portions are searched rather than the full transcripts themselves, reducing complexity compared to searching entire transcripts.
This argument is not entirely convincing because, suppose a human is looking transcript portions with the topic “greeting” with the parameter that the particular word “hello” is used, and the outcome that it is answered with a greeting back from the other party. It would be ridiculous for the human to visually scan the entire transcript for this; obviously one could check the beginning of each transcript and skip the rest. This argument might be more convincing if the claim required processing a large amount of lengthy transcripts looking for topics/parameters/outcomes which might be only buried in the middle, but as currently drafted, this is not a requirement in the particular claim language.
On pages 11-12, regarding the 35 U.S.C. 102(a)(1) rejections, Applicant argues that the claims are patentable over Meteer allegedly because nothing in Meteer mentions searching any transcript portions that are each cross-correlated with three things: machine-identified corresponding portion topics, machine-identified corresponding portion parameters, and machine-identified corresponding portion outcomes.
In response, the examiner respectfully disagrees. Meteer at [0099] describes:[0099] The communications selector interface window 760 can enable an administrator of the contact center analysis system to select some or all communications flowing through the contact center analysis system for review and analysis. The administrator can sort, filter, or otherwise organize a collection of communications according to various criteria, such as a keyword search on the text of the communications; case number; CSR information; customer information (e.g., area code, geographic region, and other location information; age, gender, years of education, and other demographic information); time and date of the communications; duration of the communications; communication channel of the communications; outcomes of the communications (e.g., whether the customer's issue was resolved or unresolved, the total number of communications to resolve the customer's issues, total length of time spent to resolve the customer's issue, and other information relating to the outcome); reason for the communications (e.g., business department contacted, product line, and other information relating to the source of the customer's issue); events or waypoints included in or excluded from the communications; and other features and characteristics of communications discussed elsewhere in the present disclosure.
In other words, Meteer describes allowing the administrator to filter communications by topic, parameters, and outcomes, as well as keyword search. The specific use of the term “filter” along with the user interface shown in Fig. 7 make clear that the keywords and other filters are applied in conjunction. The portion topics, parameters, and outcomes used to filter the transcripts are machine identified ([0085], [0063], [0099], [0106]).
The arguments on pages 12 regarding independent claims 8 and 15, as well as dependent claims 4, 7, 11, 14, and 18 are similar to those addressed above, and are not persuasive for similar reasons.
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 “identifying, by a machine-learning (ML) model, portion topics, corresponding portion parameters, and corresponding portion outcomes of transcript portions in one or more transcripts within which each of multiple transcript portions has a corresponding portion topic detailed by a corresponding portion parameter and by a corresponding portion outcome, the ML model being trained based on training topics, training parameters, and training outcomes; performing, by one or more processors, a search of the multiple transcript portions for each of which the ML model identified the corresponding portion topic, the corresponding portion parameter, and the corresponding portion outcome, the search of the multiple transcript portions being 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 identifying, by a machine-learning (ML) model, portion topics, corresponding portion parameters, and corresponding portion outcomes of transcript portions in one or more transcripts within which each of multiple transcript portions has a corresponding portion topic detailed by a corresponding portion parameter and by a corresponding portion outcome, the ML model being trained based on training topics, training parameters, and training outcomes, as drafted, is a process that, under its broadest reasonable interpretation, covers performance of the limitation in the mind for the recitation of generic computer components. For example, but for the “by a machine-learning (ML) model…, the ML model being trained based on training topics, training parameters, and training outcomes” language, “identifying… portion topics, corresponding portion parameters, and corresponding portion outcomes of transcript portions in one or more transcripts within which each of multiple transcript portions has a corresponding portion topic detailed by a corresponding portion parameter and by a corresponding portion outcome” in the context of this claim encompasses visually reading a transcript and mentally identifying portion topics, corresponding portion parameters, and corresponding portion outcomes of transcript portions in one or more transcripts with multiple transcript portions having topics with parameters and outcomes.
Similarly, the limitation of “performing, by one or more processors, a search of the multiple transcript portions for each of which the ML model identified the corresponding portion topic, the corresponding portion parameter, and the corresponding portion outcome, the search of the multiple transcript portions being 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, but for “by one or more processors”, covers performance of the limitation in the mind. For example, “performing, …, a search of the multiple transcript portions for each of which the ML model identified the corresponding portion topic, the corresponding portion parameter, and the corresponding portion outcome, the search of the multiple transcript portions being 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 portions 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” and “one or more processors”. 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, and a generic one or more processors) 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 have 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, and 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 and using one or more processors to perform the search amounts 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. 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 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 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 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 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:
identifying, by a machine-learning (ML) model, portion topics, corresponding portion parameters, and corresponding portion outcomes of transcript portions in one or more transcripts within which each of multiple transcript portions has a corresponding portion topic detailed by a corresponding portion parameter and by a corresponding portion outcome (classifier, a ML model, identifies waypoints within segments, Fig 4, [0085], the waypoints having topic labels, Table 1, [0063], and outcomes for resolution waypoints, [0099], [0106]), the ML model being trained based on training topics, training parameters, and training outcomes (the ML model trained 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]);
performing, by one or more processors, a search of the multiple transcript portions for each of which the ML model identified the corresponding portion topic, the corresponding portion parameter, and the corresponding portion outcome, the search of the multiple transcript portions being 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; in order to retrieve any keyword results for a particular outcome, reason for the communication, customer name, this is considered a conjunction of a keyword topic and parameter or outcome filter for those transcript portions matching those criteria together); 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:
identifying, by a machine-learning (ML) model, portion topics, corresponding portion parameters, and corresponding portion outcomes of transcript portions in one or more transcripts within which each of multiple transcript portions has a corresponding portion topic detailed by a corresponding portion parameter and by a corresponding portion outcome (classifier, a ML model, identifies waypoints within segments, Fig 4, [0085], the waypoints having topic labels, Table 1, [0063], and outcomes for resolution waypoints, [0099], [0106]), the ML model being trained based on training topics, training parameters, and training outcomes (the ML model trained 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]);
performing, by one or more processors, a search of the multiple transcript portions for each of which the ML model identified the corresponding portion topic, the corresponding portion parameter, and the corresponding portion outcome, the search of the multiple transcript portions being 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; in order to retrieve any keyword results for a particular outcome, reason for the communication, customer name, this is considered a conjunction of a keyword topic and parameter or outcome filter for those transcript portions matching those criteria together); 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:
identifying, by a machine-learning (ML) model, portion topics, corresponding portion parameters, and corresponding portion outcomes of transcript portions in one or more transcripts within which each of multiple transcript portions has a corresponding portion topic detailed by a corresponding portion parameter and by a corresponding portion outcome (classifier, a ML model, identifies waypoints within segments, Fig 4, [0085], the waypoints having topic labels, Table 1, [0063], and outcomes for resolution waypoints, [0099], [0106]), the ML model being trained based on training topics, training parameters, and training outcomes (the ML model trained 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]);
performing, by one or more processors, a search of the multiple transcript portions for each of which the ML model identified the corresponding portion topic, the corresponding portion parameter, and the corresponding portion outcome, the search of the multiple transcript portions being 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; in order to retrieve any keyword results for a particular outcome, reason for the communication, customer name, this is considered a conjunction of a keyword topic and parameter or outcome filter for those transcript portions matching those criteria together); 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 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 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 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 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 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 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 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 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 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 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 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
THIS ACTION IS MADE FINAL. 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 nonprovisional extension fee (37 CFR 1.17(a)) 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 mailing date of this final action.
Any inquiry concerning this communication or earlier communications from the examiner should be directed to Jesse Pullias whose telephone number is 571/270-5135. The examiner can normally be reached on M-F 8:00 AM - 4:30 PM. The examiner’s fax number is 571/270-6135.
Examiner interviews are available via telephone, in-person, and video conferencing using a USPTO supplied web-based collaboration tool. To schedule an interview, applicant is encouraged to use the USPTO Automated Interview Request (AIR) at http://www.uspto.gov/interviewpractice.
If attempts to reach the examiner by telephone are unsuccessful, the examiner's supervisor, Andrew Flanders can be reached on 571/272-7516.
Information regarding the status of published or unpublished applications may be obtained from Patent Center. Unpublished application information in Patent Center is available to registered users. To file and manage patent submissions in Patent Center, visit: https://patentcenter.uspto.gov. Visit https://www.uspto.gov/patents/apply/patent-center for more information about Patent Center and https://www.uspto.gov/patents/docx for information about filing in DOCX format. For additional questions, contact the Electronic Business Center (EBC) at 866-217-9197 (toll-free). If you would like assistance from a USPTO Customer Service Representative, call 800-786-9199 (IN USA OR CANADA) or 571-272-1000.
/Jesse S Pullias/
Primary Examiner, Art Unit 2655 09/17/26
Prosecution Insights