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
1. This Office Action is in response to the Amendment filed on March 24, 2026, which paper has been placed of record in the file.
2. Claims 1-26 are pending in this application.
Claim Rejections - 35 USC § 101
3. 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.
4. Claims 1-26 are rejected under 35 U.S.C. 101 because the claim invention is directed to a judicial exception (i.e., law of nature, natural phenomenon, or abstract idea) without significantly more.
Regarding independents claim 1, which are analyzing as the following:
Step 1: This part of the eligibility analysis evaluates whether the claim falls within any statutory category. See MPEP 2106.03. The claim recites a method for personalized screen recording. Thus, the claim is to a process, which is one of the statutory categories of invention. (Step 1: YES).
Step 2A, Prong One: This part of the eligibility analysis evaluates whether the claim recites a judicial exception. As explained in MPEP 2106.04, subsection II, a claim “recites” a judicial exception when the judicial exception is “set forth” or “described” in the claim.
The claim recites the method for predicting the necessity of screen recording for each agent. The method involves contact centers monitor interactions between agents and customers from evaluation purposes and follow up actions, such as coaching plans, agents’ performance improvement. The monitoring for evaluation of the agents’ performance during interactions maybe based on calls recording and screen recordings of events that took place during the interaction (see Specification at least para [0001]). The claim recites the steps: for one or more the interactions, calculating an evaluation likelihood value, for one or more of the remote computing devices, calculating a recording percentage, based on one or more of the calculate like likelihood value and the calculated recording percentages, optimizing a usage of storage resources comprising: performing at least one of: recoding one or more data items from one or more interactions and deleting one or more data items, under its broadest reasonable interpretation when read in light of the Specification, falls within “Certain Methods of Organizing Human Activity” grouping of abstract ideas as they cover performance of managing personal behavior or relationships or interactions between people including social activities, teaching, and following rules or instructions.
The claim recites the steps of: for one or more the interactions, calculating an evaluation likelihood value, for one or more of the remote computing devices, calculating a recording percentage, based on one or more of the calculate like likelihood value and the calculated recording percentages, optimizing a usage of storage resources comprising: performing at least one of: recoding one or more data items from one or more interactions and deleting one or more data items, as drafted, is a process that, under its broadest reasonable interpretation when read in light of the Specification, covers performance of the limitations in the mind, can be practically performed by human in their mind or with pen/paper, but for the recitation of generic computer components. That is, other than reciting “a computer/processor/automatically”, nothing in the claim elements preclude the steps from practically being performed in the mind. The mere nominal recitation of generic computing devices does not take the claim limitation out of the Mental Processes grouping of abstract ideas. Thus, if a claim limitation, under its broadest reasonable interpretation, covers performance of the limitation in the mind, then it falls within the “Mental Processes” grouping of abstract ideas (concepts performed in the human mind including an observation, evaluation, judgment, opinion). See MPEP 2106.04(a)(2), subsection III.
Therefore, the claim recites an abstract idea. (Step 2A, Prong One: YES).
Step 2A, Prong Two: This part of the eligibility analysis evaluates whether the claim as a whole integrates the recited judicial exception into a practical application of the exception or whether the claim is “directed to” the judicial exception. This evaluation is performed by (1) identifying whether there are any additional elements recited in the claim beyond the judicial exception, and (2) evaluating those additional elements individually and in combination to determine whether the claim as a whole integrates the exception into a practical application. See MPEP 2106.04(d).
The claim recites the additional elements of “a communication interface to communicate via a communication network with one or more remote computing devices.” The claim also recites that the steps of “for one or more the interactions, calculating an evaluation likelihood value, for one or more of the remote computing devices, calculating a recording percentage, based on one or more of the calculate like likelihood value and the calculated recording percentages, optimizing a usage of storage resources comprising: performing at least one of: recoding one or more data items from one or more interactions and deleting one or more data items”, are performed by one or more processors.
The limitations “a communication interface to communicate via a communication network with one or more remote computing devices” are mere data transmitting and receiving, recited at a high level of generality, and thus are insignificant extra-solution activity. See MPEP 2106.05(g) (“whether the limitation is significant”). In addition, all uses of the recited judicial exceptions require such data transmitting and receiving, and, as such, these limitations do not impose any meaningful limits on the claim. These limitations amount to necessary data gathering and outputting. See MPEP 2106.05. Moreover, these additional elements do not provide any improvement to the technology, improvement to the functioning of the computer, improvement to the communication interface/communication network/remote computing devices, they are just merely used as general means for collecting and transmitting data.
Further, limitations “for one or more the interactions, calculating an evaluation likelihood value, for one or more of the remote computing devices, calculating a recording percentage, based on one or more of the calculate like likelihood value and the calculated recording percentages, optimizing a usage of storage resources comprising: performing at least one of: recoding one or more data items from one or more interactions and deleting one or more data items” are recited as being performed by the processors. The processors are recited at a high level of generality and is used to perform an abstract idea, as discussed above in Step 2A, Prong One, such that it amounts to no more than mere instructions to apply the exception using a generic computer. See MPEP 2106.05(f). The additional elements recite generic computer components the processors, a memory, and software programming instructions that are recited a high-level of generality that merely perform, conduct, carry out, implement, and/or narrow the abstract idea itself. Accordingly, the additional elements evaluated individually and in combination do not integrate the abstract idea into a practical application because they comprise or include limitations that are not indicative of integration into a practical application such as adding the words "apply it" (or an equivalent) with the judicial exception, or mere instructions to implement an abstract idea on a computer, or merely uses a computer as a tool to perform an abstract idea -- See MPEP 2106.05(f).
Even when viewed in combination, these additional elements do not integrate the recited judicial exception into a practical application (Step 2A, Prong Two: NO), and the claim is directed to the judicial exception (Step 2A, Prong One: YES).
Step 2B: This part of the eligibility analysis evaluates whether the claim as a whole, amounts to significantly more than the recited exception i.e., whether any additional element, or combination of additional elements, adds an inventive concept to the claim. See MPEP 2106.05.
Additional elements “a communication interface to communicate via a communication network with one or more remote computing devices” were found to be insignificant extra-solution activity in Step 2A, Prong Two, because they were determined to be insignificant limitations as necessary data gathering and outputting. However, a conclusion that an additional element is insignificant extra solution activity in Step 2A, Prong Two should be re-evaluated in Step 2B. See MPEP 2106.05, subsection I.A. At Step 2B, the evaluation of the insignificant extra-solution activity consideration takes into account whether or not the extra-solution activity is well understood, routine, and conventional in the field. See MPEP 2106.05(g).
As discussed in Step 2A, Prong Two above, the recitations of “a communication interface to communicate via a communication network with one or more remote computing devices” are recited at a high level of generality. These elements amount to transmitting and receiving data over a network and are well-understood, routine, conventional activity. See MPEP 2106.05(d), subsection II. The courts have recognized the following computer functions as well understood, routine, and conventional functions when they are claimed in a merely genetic manner (e.g., at a high level of generality) or as insignificant extra-solution activity: Receiving or transmitting data over a network, e.g., using the Internet to gather data, Symantec, 838 F.3d at 1321, 120 USPQ2d at 1362 (utilizing an intermediary computer to forward information); TLI Communications LLC v. AV Auto. LLC, 823 F.3d 607, 610, 118 USPQ2d 1744, 1745 (Fed. Cir. 2016) (using a telephone for image transmission); OIP Techs., Inc., v. Amazon.com, Inc., 788 F.3d 1359, 1363, 115 USPQ2d 1090, 1093 (Fed. Cir. 2015) (sending messages over a network); buySAFE, Inc. v. Google, Inc., 765 F.3d 1350, 1355, 112 USPQ2d 1093, 1096 (Fed. Cir. 2014) (computer receives and sends information over a network).
As discussed in Step 2A, Prong Two above, the recitation of the one or more processors to perform limitations “for one or more the interactions, calculating an evaluation likelihood value, for one or more of the remote computing devices, calculating a recording percentage, based on one or more of the calculate like likelihood value and the calculated recording percentages, optimizing a usage of storage resources comprising: performing at least one of: recoding one or more data items from one or more interactions and deleting one or more data items”, amounts to no more than mere instructions to apply the exception using a generic computer component.
Even when considered in combination, these additional elements represent mere instructions to implement an abstract idea or other exception on a computer and insignificant extra-solution activity, which do not provide an inventive concept. Therefore, the claim is not patent eligible. (Step 2B: NO).
Regarding independent claims 9 and 17, Alice Corp. establishes that the same analysis should be used for all categories of claims. Therefore, independent claim 9 directed to a system, independent claim 17 directed to a method, are also rejected as ineligible subject matter under 35 U.S.C. 101 for substantially the same reasons as independent method claim 1.
Dependent claims 2-8, 10-16, and 18-26, have been given the full two-part analysis, analyzing the additional limitations both individually and in combination. The dependent claims, when analyzed individually and in combination, are also held to be patent- ineligible under 35 U.S.C. 101.
Regarding dependent claims 2, 10, and 18, the claims simply refine the abstract idea by further reciting determining a recording policy based on one or more of the features and the calculated recording percentages …, that fall under the category of Organizing Human activity and mental process groupings of abstract ideas as described above in the independent claim 1. Thus, the dependent claims do not add any additional element or subject matter that provides a technological improvement (i.e., an integration into a practical application under Step 2A-Prong Two), results in the claim being directed to patent eligible subject matter or include an element or feature that is significantly more than the recited abstract idea (i.e., a technological inventive concept under Step 2B).
Regarding dependent claims 3 and 11, the claims simply refine the abstract idea by further reciting wherein one or more of the features comprise one or more: a handling time per interaction, a task duration after interaction…, that fall under the category of Organizing Human activity and mental process groupings of abstract ideas as described above in the independent claim 1. Thus, the dependent claims do not add any additional element or subject matter that provides a technological improvement (i.e., an integration into a practical application under Step 2A-Prong Two), results in the claim being directed to patent eligible subject matter or include an element or feature that is significantly more than the recited abstract idea (i.e., a technological inventive concept under Step 2B).
Regarding dependent claims 4 and 12, the claims simply refine the abstract idea by further reciting storing one or more of the data items from one or more of the interactions in the data store…, that fall under the category of mental process and Organizing Human Activity groupings of abstract ideas as described above in the independent claim 1. Thus, the dependent claims do not add any additional element or subject matter that provides a technological improvement (i.e., an integration into a practical application under Step 2A-Prong Two), results in the claim being directed to patent eligible subject matter or include an element or feature that is significantly more than the recited abstract idea (i.e., a technological inventive concept under Step 2B).
Regarding dependent claims 5, 13, and 19, the claims recite assembling one or more feature vectors, clustering one or more of the feature vectors, one or more interaction classification models, that fall under the category of Mathematical concepts grouping of abstract ideas (mathematical relationships, mathematical formulas or equations, mathematical calculations).
The limitation “training one or more interaction classification models based on the clustering” provides nothing more than mere instructions to implement an abstract idea on a generic computer. See MPEP 2106.05(f). MPEP 2106.05(f) provides the following considerations for determining whether a claim simply recites a judicial exception with the words “apply it” (or an equivalent), such as mere instructions to implement an abstract idea on a computer: (1) whether the claim recites only the idea of a solution or outcome i.e., the claim fails to recite details of how a solution to a problem is accomplished; (2) whether the claim invokes computers or other machinery merely as a tool to perform an existing process; and (3) the particularity or generality of the application of the judicial exception.
The element “training one or more interaction classification models based on the clustering” is used to generally apply the abstract idea without placing any limits on how the training classification models functions. Rather, these limitations only recite the outcome of “classifying models” and do not include any details about how the “training classification models” is accomplished. See MPEP 2106.05(f). Moreover, the recitation of “training one or more interaction classification models based on the clustering” also merely indicates a field of use or technological environment in which the judicial exception is performed. Although the additional element “training one or more interaction classification models based on the clustering” limits the identified judicial exceptions “classifying models based on the clustering,” this type of limitation merely confines the use of the abstract idea to a particular technological environment (training models) and thus fails to add an inventive concept to the claims. See MPEP 2106.05(h).
Thus, the dependent claims do not add any additional element or subject matter that provides a technological improvement (i.e., an integration into a practical application under Step 2A-Prong Two), results in the claim being directed to patent eligible subject matter or include an element or feature that is significantly more than the recited abstract idea (i.e., a technological inventive concept under Step 2B).
Regarding dependent claims 6 and 14, the claims simply refine the abstract idea by further reciting wherein the calculating of a recording percentage comprises normalizing one or more of the recording percentage…, that fall under the category of Organizing Human Activity and mental process groupings of abstract ideas as described above in the independent claim 1. Thus, the dependent claims do not add any additional element or subject matter that provides a technological improvement (i.e., an integration into a practical application under Step 2A-Prong Two), results in the claim being directed to patent eligible subject matter or include an element or feature that is significantly more than the recited abstract idea (i.e., a technological inventive concept under Step 2B).
Regarding dependent claims 7 and 15, the claims simply refine the abstract idea by further reciting wherein ate least one of: the calculating of an evaluation likelihood value… is performed periodically, that fall under the category of Organizing Human Activity and mental process grouping of abstract ideas as described above in the independent claim 1. Thus, the dependent claims do not add any additional element or subject matter that provides a technological improvement (i.e., an integration into a practical application under Step 2A-Prong Two), results in the claim being directed to patent eligible subject matter or include an element or feature that is significantly more than the recited abstract idea (i.e., a technological inventive concept under Step 2B).
Regarding dependent claims 8 and 16, the claims recite: receiving one or more data items from one or more of the remote computing devices via the communication network, are additional elements that are mere data receiving and transmitting recited at a high level of generality, and thus are insignificant extra-solution activity. See MPEP 2106.05(g) (“whether the limitation is significant”). In addition, all uses of the recited judicial exceptions require such data transmitting and receiving, and, as such, these limitations do not impose any meaningful limits on the claim. These limitations amount to necessary data receiving and transmitting. See MPEP 2106.05. These elements amount to receiving or transmitting data over a network and are well-understood, routine, conventional activity. See MPEP 2106.05(d), subsection II. Thus, the dependent claims do not add any additional element or subject matter that provides a technological improvement (i.e., an integration into a practical application under Step 2A-Prong Two), results in the claim being directed to patent eligible subject matter or include an element or feature that is significantly more than the recited abstract idea (i.e., a technological inventive concept under Step 2B).
Regarding dependent claim 20, the claim simply refines the abstract idea by further reciting comparing one or more of the predicted likelihood values with one or more observed values, that fall under the category of Organizing Human Activity and mental process grouping of abstract ideas as described above in the independent claim 1. Thus, the dependent claim does not add any additional element or subject matter that provides a technological improvement (i.e., an integration into a practical application under Step 2A-Prong Two), results in the claim being directed to patent eligible subject matter or include an element or feature that is significantly more than the recited abstract idea (i.e., a technological inventive concept under Step 2B).
Regarding dependent claims 21-22, the claims simply refine the abstract idea by further reciting wherein the evaluation likelihood value is used to determine whether an interaction of one or more of the interactions will be chosen for evaluation; and wherein the evaluation likelihood value is used to determine an amount of interactions to be selected for evaluation by a supervisor, that fall under the category of Organizing Human Activity and mental process groupings of abstract ideas as described above in the independent claim 1. Thus, the dependent claim does not add any additional element or subject matter that provides a technological improvement (i.e., an integration into a practical application under Step 2A-Prong Two), results in the claim being directed to patent eligible subject matter or include an element or feature that is significantly more than the recited abstract idea (i.e., a technological inventive concept under Step 2B).
Regarding dependent claim 23, the claim simply refines the abstract idea by further reciting wherein the calculating of the evaluation likelihood value comprises adjusting one or more weights, each weight assigned to a corresponding feature of the one or more features”, that fall under the category of Organizing Human Activity and mental process groupings of abstract ideas as described above in the independent claim 1. Moreover, the claim recites the additional element “adjusting by a machine learning model” which provides nothing more than mere instructions to implement an abstract idea on a generic computer. See MPEP 2106.05(f). MPEP 2106.05(f) provides the following considerations for determining whether a claim simply recites a judicial exception with the words “apply it” (or an equivalent), such as mere instructions to implement an abstract idea on a computer: (1) whether the claim recites only the idea of a solution or outcome i.e., the claim fails to recite details of how a solution to a problem is accomplished; (2) whether the claim invokes computers or other machinery merely as a tool to perform an existing process; and (3) the particularity or generality of the application of the judicial exception.
The additional element “adjusting, by a machine learning model” is used to generally apply the abstract idea and invokes the computer merely as a tool to perform an existing process. See MPEP 2106.05(f).
The additional element “adjusting, by a machine learning model” is used to generally apply the abstract idea without placing any limits on how the machine learning functions. Rather, this limitation only recites the outcome of “adjusting one or more weighs” and does not include any details about how the solution is accomplished. See MPEP 2106.05(f).
The additional element “adjusting, by a machine learning model” also merely indicates a field of use or technological environment in which the judicial exception is performed. Although the additional element “adjusting, by a machine learning model” limits the identified judicial exceptions “adjusting one or more weighs”, this type of limitation merely confines the use of the abstract idea to a particular technological environment (machine learning) and thus fails to add an inventive concept to the claims. See MPEP 2106.05(h).
Thus, the dependent claim does not add any additional element or subject matter that provides a technological improvement (i.e., an integration into a practical application under Step 2A-Prong Two), results in the claim being directed to patent eligible subject matter or include an element or feature that is significantly more than the recited abstract idea (i.e., a technological inventive concept under Step 2B).
Therefore, the dependent claims do not impart patent eligibility to the abstract idea of the independent claim. The dependent claims rather further narrow the abstract idea and the narrower scope does not change the outcome of the two-part Mayo test. Narrowing the scope of the claims is not enough to impart eligibility as it is still interpreted as an abstract idea, a narrower abstract idea. Therefore, none of the dependent claims alone or as an ordered combination add limitations that qualify as significantly more than the abstract idea.
Regarding dependent claims 24-26, the claims simply refine the abstract idea by further reciting automatically delete one or more of the data items from the data store., that fall under the category of Organizing Human Activity and mental process groupings of abstract ideas as described above in the independent claim 1. Thus, the dependent claim does not add any additional element or subject matter that provides a technological improvement (i.e., an integration into a practical application under Step 2A-Prong Two), results in the claim being directed to patent eligible subject matter or include an element or feature that is significantly more than the recited abstract idea (i.e., a technological inventive concept under Step 2B).
Accordingly, claims 1-26 are not draw to eligible subject matter as they are directed to an abstract idea without significantly more and are rejected under 35 USC § 101 as being directed to non-statutory subject matter.
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.
Claim Rejections - 35 USC § 103
5. 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.
6. Claims 1-26 are rejected under 35 U.S.C. 103 as being unpatentable over Miller et al. (hereinafter Miller, US 2018/0091654) in view of Toksos et al. (hereinafter Toksos, US 2019/0286461).
Regarding to claim 1, Miller discloses a computerized method for personalized screen recording, the computerized-method comprising:
in a computerized system comprising one or more processors, a communication interface to communicate via a communication network with one or more remote computing devices, and a memory including a data store of a plurality of data items, the data items describing a plurality of interactions involving one or more of the remote computing devices (para [0138], FIG. 5C, the central processing unit 1521 may include multiple processors P1, P2, P3, P4, and may provide functionality for simultaneous execution of instructions; para [0141], The computing device 1500 may include a network interface 1518 to interface to the network 1504 through a variety of connections); para [0133], The removable media interface 1516 may for example be used for installing software and programs. The computing device 1500 may further include a storage device 1528);
for one or more of the interactions, calculating an evaluation likelihood value based on one or more features associated with one or more of the data items (para [0037], The evaluation of the interaction may result in a score that reflects the agent's performance on that interaction. The score may be used, with other scores and/or other performance metrics, to compute one or more aggregate scores that may reflect the agent's overall performance. Individual scores or an aggregate score may be used to indicate that an agent has met, exceeded, or failed to meet particular standards of performance), wherein the evaluation likelihood value is calculated based on historical data elements, the historical data elements indicating whether a past interaction was chosen for evaluation (para [0091], the one or more prediction models are trained by a model training module 175 based on historical recorded interactions and the scores Y assigned to those interactions by human supervisors; para [0093], When generating training data for training the prediction module, the manually generated score or scores for a given interaction is retrieved from the quality service 176, and the corresponding historical interaction is retrieved from the mass storage device 126 and supplied to the interaction feature extractor 174 to extract the features for the historical interaction. The combinations of historical interaction features and their associated evaluations represent individual data points of a set of training data. For example, where the evaluation may include multiple scores (e.g., m scores), then the set of scores may be represented as the vector (Y.sub.1, Y.sub.2, . . . , Y.sub.m), and the combination of the feature vector of a historical interaction (the historical interaction features) and its associated scores may be represented as {(X.sub.1, X.sub.2, . . . , X.sub.n), (Y.sub.1, Y.sub.2, . . . , Y.sub.m)}. para [0120], the condition may be based on using a particular agent's past performance predict an agent's current performance. The controller may then compare the predicted performance with the automatically evaluated performance on a recent interaction with to detect deviations in the agent's performance from history, which may show signs of worsening performance (and resulting in the automatic scheduling of additional training) or improving performance (where the agent may be sent a message congratulating him or her on the improvement and encouraging continued progress));
for one or more of the remote computing devices, calculating a recording percentage based on the calculated likelihood values (para [0020], The aggregate may correspond to a percentage of the recorded interactions during the time window that satisfy an individual interaction threshold condition, and the threshold may correspond to a percentage of recorded interactions satisfying the individual interaction threshold condition); and
based on one or more of the calculated likelihood values and the calculated recording percentages, performing at least one of: automatically recording one or more data items from one or more interactions on one or more of the remote computing devices, and automatically deleting one or more of the data items from the data store (para [0060], The contact center system may further include a quality management server 170 configured to provide quality monitoring of agents of the contact center. The quality management server 162 may provide a user interface for human supervisors to evaluate agents, such as by reviewing one or more recorded interactions associated with an agent (or providing real-time review of an agent while the agent is participating in the interaction), an automatic evaluation module for automatically analyzing an interaction, and an action module for generating actions in response to computing particular scores associated with an interaction or interactions, where the actions may include deleting the associated interaction (e.g., removing it from the mass storage device 126), setting an expiration date for the recording (e.g., a date on which the recording should be deleted), marking the recording to be preserved).
Miller does not disclose, however, Toksos discloses:
optimizing a usage of storage resources in the computerized system, the optimizing to save storage space, wherein the optimized usage of storage resources comprises performing: automatically recording one or more data items from one or more interactions on one or more of the remote computing devices (para [0008], By selecting and presenting the appropriate video for the application, the present disclosure may thus reduce the time of execution of an application by training the user to properly use and run the application, thereby saving client-side power consumption, network bandwidth, and computer processing time. Additionally, by optimizing selection of the recorded video, the server may discard low-performing videos, saving server-side storage space, and allowing other computing devices (e.g. edge caching servers, content redistribution systems, etc.) to cache and retransmit the selected video, reducing server bandwidth consumption; para [0126], the system 200 can automatically generate recordings 110 of the application 238 executing on multiple client devices 110 and then select the most appropriate or optimal recording 110 from the recordings 110 to instruct or train a user at the client device 105 to operate the application 238. As such, the system 200 detailed herein may thus reduce the time of execution of an application 238 by training the user to properly use and run the application 238, thereby saving power consumption, network bandwidth, and computer processing time at the client device 105, data processing system 210, and the network 205; para [0137], The data processing system 210 can receive the interaction log (step 362). The data processing system 210 can then proceed to traverse through the interactions recorded on the interaction log in steps 364-374 to update the performance metric for the video. The data processing system 210 can identify a type of interaction (step 364). The data processing system 210 can identify coordinates of interaction (step 366). The data processing system 210 can identify a time of interaction (step 368).).
Therefore, it would have been obvious to one with ordinary skill in the art before the effective filing date of the claimed invention to modify the Miller’s to incorporate the features taught by Toksos above, for the purpose of saving storage space in computer system. Since Miller discloses automatically recording one or more data items from one or more interactions on one or more of the remote computing devices (see para [0113], a system administrator may also set rules or policies that establish conditions for retaining the interaction, deleting the interaction, archiving the interaction, and the like. In some embodiments, retained interactions may be provided with an expiration date (such as two weeks from the date of recording), and after expiration, the retained interaction is deleted), Toksos teaches optimizing a usage of storage resources in the computerized system to save storage space by automatically recording one or more data items from one or more interactions on one or more of the remote computing devices, as described above, therefore, one of ordinary skill in the art would have recognized that the combination of Miller and Toksos would have yield predictable results in saving storage space in computer system.
Regarding to claim 2, Miller discloses the computerized method of claim 1, comprising, for one or more remote computers, determining a recording policy based on one or more of the features and the calculated recording percentages, wherein one or more of the data items includes one or more of: agents’ data, agents’ metrics, and one or more of the historical data elements; and wherein the recording one or more data items or the deleting one or more of the data items are performed based on the policy (para [0113], in the context of managing recordings of interactions, a system administrator may also set rules or policies that establish conditions for retaining the interaction, deleting the interaction, archiving the interaction, and the like. In some embodiments, retained interactions may be provided with an expiration date (such as two weeks from the date of recording), and after expiration, the retained interaction is deleted; para [0093], When generating training data for training the prediction module, the manually generated score or scores for a given interaction is retrieved from the quality service 176, and the corresponding historical interaction is retrieved from the mass storage device 126 and supplied to the interaction feature extractor 174 to extract the features for the historical interaction).
Regarding to claim 3, Miller discloses the computerized method of claim 2, wherein one or more of the features comprise one or more of: a handling time per interaction, a task duration after interaction, an interaction length, a number of interactions per timeframe, an interaction hold count, an interaction transfer count, a number of participants per interaction, a channel type, one or more of the agent data or metrics, a number of recording playbacks, one or more interaction scores, and one or more manually labeled categories (para [0017], a threshold condition detected in operation 350 may be a percentage of “bad” interactions over a time period, where a “bad” interaction corresponds to a particular score being below an individual interaction threshold level).
Regarding to claim 4, Miller discloses the computerized method of claim 2, comprising storing one or more of the data items from one or more of the interactions in the data store, wherein one or more of the data items from one or more of the interactions include one or more of: voice interaction data, digital interaction data, screen information data, and interaction metadata (para [0087], automatically extracting interaction features based on the metadata about the interaction. Some of these metadata may include the number of transfers of the interaction between agents, customer feedback (e.g., a net promoter score (NPS) or survey data), the time of day of the interaction, and conversation length (e.g., the number of chat messages sent, the number of emails sent, the total amount of text sent between the customer and agent, the duration of the text chat session or the audio or video conference)).
Regarding to claim 5, Miller discloses the computerized method of claim 2, wherein the determining of a recording policy comprises:
assembling one or more feature vectors based on one or more of the features (para [0093], For example, where the evaluation may include multiple scores (e.g., m scores), then the set of scores may be represented as the vector (Y.sub.1, Y.sub.2, . . . , Y.sub.m), and the combination of the feature vector of a historical interaction (the historical interaction features) and its associated scores may be represented as {(X.sub.1, X.sub.2, . . . , X.sub.n), (Y.sub.1, Y.sub.2, . . . , Y.sub.m)}.
clustering one or more of the feature vectors (para [0083], the feature extractor 174 performs expectation maximization clustering on the words that were transformed into vectors and uses the cluster outcomes as features (e.g., tagging with particular “topics”)); and
training one or more interaction classification models based on the clustering (para [0094], The training data may then be used to train, validate, and test the one or more prediction models. Each prediction model may be used to predict an answer or a score for a corresponding one of the questions of the evaluation form (e.g., for a particular score Y.sub.i of the evaluation form)).
Regarding to claim 6, Miller discloses the computerized method of claim 1, wherein the calculating of a recording percentage comprises normalizing one or more of the recording percentage based on one or more predetermined thresholds and one or more normalization factors (para [0011], The aggregate may correspond to a percentage of the recorded interactions during the time window that satisfy an individual interaction threshold condition, and the threshold may correspond to a percentage of recorded interactions satisfying the individual interaction threshold condition).
Regarding to claim 7, Miller discloses the computerized method of claim 5, wherein at least one of: the calculating of an evaluation likelihood value, the calculating of a recording percentage, the recording or deleting of one or more data items, the determining of a recording policy, and the storing of one or more data items is performed periodically (para [0090], the interaction features X extracted by the feature extractor 174 are returned to the quality predictor 172, which uses the extracted interaction features to automatically compute scores for the interaction in operation 330. The scores may be calculated by supplying the features to one or more prediction models that may be trained to predict the scores that a human supervisor would assign to the interaction).
Regarding to claim 8, Miller discloses the computerized method of claim 1, comprising receiving one or more data items from one or more of the remote computing devices via the communication network (para [0056], the contact center system may include a universal contact server (UCS) 127, configured to retrieve information stored in the CRM database and direct information to be stored in the CRM database. The UCS 127 may also be configured to facilitate maintaining a history of customers' preferences and interaction history, and to capture and store data regarding comments from agents, customer communication history).
Regarding to claims 9-16, Miller discloses a computerized system for analyzing data representing remotely connected computer systems, the system comprising:
one or more processors (para [0138], FIG. 5C, the central processing unit 1521 may include multiple processors P1, P2, P3, P4, and may provide functionality for simultaneous execution of instructions);
a communication interface to communicate via a communication network with one or more remote computing devices (para [0141], The computing device 1500 may include a network interface 1518 to interface to the network 1504 through a variety of connections); and
a memory including a data store of a plurality of data items, the data items describing a plurality of interactions involving one or more of the remote computing devices (para [0133], The removable media interface 1516 may for example be used for installing software and programs. The computing device 1500 may further include a storage device 1528);
wherein the one or more processors are to: perform the method described in claims 1-8 above, therefore, are rejected by the same rationale.
Regarding to claim 17, Miller discloses a computerized method for intelligent optimization of storage usage, the computerized- method comprising:
in a computerized system comprising one or more processors, a communication interface to communicate via a communication network with one or more remote computing devices, and a memory including a data store of a plurality of data items (para [0138], FIG. 5C, the central processing unit 1521 may include multiple processors P1, P2, P3, P4, and may provide functionality for simultaneous execution of instructions; para [0141], The computing device 1500 may include a network interface 1518 to interface to the network 1504 through a variety of connections); para [0133], The removable media interface 1516 may for example be used for installing software and programs. The computing device 1500 may further include a storage device 1528);
for one or more of the data items, predicting an evaluation likelihood value based on one or more features associated with one or more of the data items (para [0090], The interaction features X extracted by the feature extractor 174 are returned to the quality predictor 172, which uses the extracted interaction features to automatically compute scores for the interaction in operation 330. The scores may be calculated by supplying the features to one or more prediction models that may be trained to predict the scores that a human supervisor would assign to the interaction), wherein the evaluation likelihood value is calculated based on historical data elements, the historical data elements indicating whether a past interaction was chosen for evaluation (para [0091], the one or more prediction models are trained by a model training module 175 based on historical recorded interactions and the scores Y assigned to those interactions by human supervisors; para [0093], When generating training data for training the prediction module, the manually generated score or scores for a given interaction is retrieved from the quality service 176, and the corresponding historical interaction is retrieved from the mass storage device 126 and supplied to the interaction feature extractor 174 to extract the features for the historical interaction. The combinations of historical interaction features and their associated evaluations represent individual data points of a set of training data. For example, where the evaluation may include multiple scores (e.g., m scores), then the set of scores may be represented as the vector (Y.sub.1, Y.sub.2, . . . , Y.sub.m), and the combination of the feature vector of a historical interaction (the historical interaction features) and its associated scores may be represented as {(X.sub.1, X.sub.2, . . . , X.sub.n), (Y.sub.1, Y.sub.2, . . . , Y.sub.m)}. para [0120], the condition may be based on using a particular agent's past performance predict an agent's current performance. The controller may then compare the predicted performance with the automatically evaluated performance on a recent interaction with to detect deviations in the agent's performance from history, which may show signs of worsening performance (and resulting in the automatic scheduling of additional training) or improving performance (where the agent may be sent a message congratulating him or her on the improvement and encouraging continued progress));
for one or more of the remote computing devices, deriving a storing percentage based on the calculated likelihood values (para [0020], The aggregate may correspond to a percentage of the recorded interactions during the time window that satisfy an individual interaction threshold condition, and the threshold may correspond to a percentage of recorded interactions satisfying the individual interaction threshold condition); and
based on one or more of the calculated likelihood values and the derived storing percentages, performing at least one of: automatically storing one or more data items from one or more interactions on one or more of the remote computing devices, and automatically deleting one or more of the data items from the data store (para [0060], The contact center system may further include a quality management server 170 configured to provide quality monitoring of agents of the contact center. The quality management server 162 may provide a user interface for human supervisors to evaluate agents, such as by reviewing one or more recorded interactions associated with an agent (or providing real-time review of an agent while the agent is participating in the interaction), an automatic evaluation module for automatically analyzing an interaction, and an action module for generating actions in response to computing particular scores associated with an interaction or interactions, where the actions may include deleting the associated interaction (e.g., removing it from the mass storage device 126), setting an expiration date for the recording (e.g., a date on which the recording should be deleted), marking the recording to be preserved).
Miller does not disclose, however, Toksos discloses:
optimizing a usage of storage resources in the computerized system, the optimizing to save storage space, wherein the optimized usage of storage resources comprises performing: automatically recording one or more data items from one or more interactions on one or more of the remote computing devices (para [0008], By selecting and presenting the appropriate video for the application, the present disclosure may thus reduce the time of execution of an application by training the user to properly use and run the application, thereby saving client-side power consumption, network bandwidth, and computer processing time. Additionally, by optimizing selection of the recorded video, the server may discard low-performing videos, saving server-side storage space, and allowing other computing devices (e.g. edge caching servers, content redistribution systems, etc.) to cache and retransmit the selected video, reducing server bandwidth consumption; para [0126], the system 200 can automatically generate recordings 110 of the application 238 executing on multiple client devices 110 and then select the most appropriate or optimal recording 110 from the recordings 110 to instruct or train a user at the client device 105 to operate the application 238. As such, the system 200 detailed herein may thus reduce the time of execution of an application 238 by training the user to properly use and run the application 238, thereby saving power consumption, network bandwidth, and computer processing time at the client device 105, data processing system 210, and the network 205; para [0137], The data processing system 210 can receive the interaction log (step 362). The data processing system 210 can then proceed to traverse through the interactions recorded on the interaction log in steps 364-374 to update the performance metric for the video. The data processing system 210 can identify a type of interaction (step 364). The data processing system 210 can identify coordinates of interaction (step 366). The data processing system 210 can identify a time of interaction (step 368).).
Therefore, it would have been obvious to one with ordinary skill in the art before the effective filing date of the claimed invention to modify the Miller’s to incorporate the features taught by Toksos above, for the purpose of saving storage space in computer system. Since Miller discloses automatically recording one or more data items from one or more interactions on one or more of the remote computing devices (see para [0113], a system administrator may also set rules or policies that establish conditions for retaining the interaction, deleting the interaction, archiving the interaction, and the like. In some embodiments, retained interactions may be provided with an expiration date (such as two weeks from the date of recording), and after expiration, the retained interaction is deleted), Toksos teaches optimizing a usage of storage resources in the computerized system to save storage space by automatically recording one or more data items from one or more interactions on one or more of the remote computing devices, as described above, therefore, one of ordinary skill in the art would have recognized that the combination of Miller and Toksos would have yield predictable results in saving storage space in computer system.
Regarding to claim 18, Miller discloses the computerized method of claim 17, comprising, for one or more remote computers, determining a recording policy based on one or more of the features and the calculated storing percentages (para [0113], in the context of managing recordings of interactions, a system administrator may also set rules or policies that establish conditions for retaining the interaction, deleting the interaction, archiving the interaction, and the like. In some embodiments, retained interactions may be provided with an expiration date (such as two weeks from the date of recording), and after expiration, the retained interaction is deleted).
Regarding to claim 19, Miller discloses the computerized-system of claim 18, wherein the determining of a recording policy comprises:
training one or more supervised classification machine learning models based on the one or more data items (para [0094], The training data may then be used to train, validate, and test the one or more prediction models. Each prediction model may be used to predict an answer or a score for a corresponding one of the questions of the evaluation form (e.g., for a particular score Y.sub.i of the evaluation form). In various embodiments of the present invention, each the prediction models may be a model such as a linear regression model, a multiple regression model, a k-nearest neighbors regression, a random forest tree, a support vector machine, or a neural network, which may be selected based on applicability to the particular portion of the evaluation to be predicted and the characteristics of the interaction features supplied to the prediction model).
Regarding to claim 20, Miller discloses the computerized-system of claim 19, comprising comparing one or more of the predicted likelihood values with one or more observed values (para [0025], The condition may include comparing the score to a threshold corresponding to a failure to comply with agent performance standards).
Regarding to claim 21, Miller discloses the computerized method of claim 1, wherein the evaluation likelihood value is used to determine whether an interaction of one or more of the interactions will be chosen for evaluation (para [0060], The quality management server 162 may provide a user interface for human supervisors to evaluate agents, such as by reviewing one or more recorded interactions associated with an agent (or providing real-time review of an agent while the agent is participating in the interaction), an automatic evaluation module for automatically analyzing an interaction, and an action module for generating actions in response to computing particular scores associated with an interaction or interactions).
Regarding to claim 22, Miller discloses the computerized method of claim 1, wherein the evaluation likelihood value is used to determine an amount of interactions to be selected for evaluation by a supervisor (para [0068], The quality management server 170 may also include a quality service 176, which is configured to provide a user interface (e.g., a web server providing a web application) for human supervisors to perform evaluations of interactions and to score the interactions based on evaluation criteria).
Regarding to claim 23, Miller does not disclose, however, Toksos discloses the computerized method of claim 1, wherein the calculating of the evaluation likelihood value comprises adjusting, by a machine learning model, one or more weights, each weight assigned to a corresponding feature of the one or more features (para [0016], generating the performance metric can further include adjusting the weight from the initial value to a second value in the aggregate performance model, responsive to receiving the generated interaction log of the one or more interactions from the second client device; para [0097], The aggregate performance model may be any model used in machine learning, such as an artificial neural network (ANN), a support vector machine (SVM), deep structural learning model, unsupervised learning models, or supervised learning models, among others; para [0111], the video selector 226 can update the performance metric or the aggregate performance model at periodic intervals. In some implementations, the video selector 226 can adjust one or more weights of the aggregate performance model to another value responsive to receiving the interaction log; para [0115], the video selector 226 can adjust, change, or otherwise update the aggregate performance model based on the type of interaction. In some implementations, using the type of interaction, the video selector 226 can adjust, change, or otherwise update the one or more weights of the aggregate performance model. In some implementations, the video selector 226 can identify a positive weight adjustment or a negative weight adjustment from the initial value or another value to the one or more weights of the aggregate performance model for the recordings 110 based on the type of interaction. The positive weight adjustment and the negative weight adjustment may be each a fixed consonant (e.g., multiplicative factor greater than or less than 1) or a range of values depending on the type of interaction).
Therefore, it would have been obvious to one with ordinary skill in the art before the effective filing date of the claimed invention to modify the Miller’s to incorporate the features taught by Toksos above, for the purpose of providing more effective in calculating of the evaluation likelihood value with the help of the machine learning model. Since Miller discloses calculating of the evaluation likelihood value, Toksos teaches calculating of the evaluation likelihood value comprises by using a machine learning model to adjust one or more weights, as described above, therefore, one of ordinary skill in the art would have recognized that the combination of Miller and Toksos would have yield predictable results in calculating the evaluation likelihood value by using a machine learning model.
Regarding to claims 24-26, Miller does not disclose, however, Toksos discloses: wherein the one or more processors is further configured to automatically delete one or more of the data items from the data store (para [0008], By selecting and presenting the appropriate video for the application, the present disclosure may thus reduce the time of execution of an application by training the user to properly use and run the application, thereby saving client-side power consumption, network bandwidth, and computer processing time. Additionally, by optimizing selection of the recorded video, the server may discard low-performing videos, saving server-side storage space, and allowing other computing devices (e.g. edge caching servers, content redistribution systems, etc.) to cache and retransmit the selected video, reducing server bandwidth consumption).
Therefore, it would have been obvious to one with ordinary skill in the art before the effective filing date of the claimed invention to modify the Miller’s to incorporate the features taught by Toksos above, for the purpose of saving storage space in computer system. Since Miller discloses automatically deleting one or more of the data items from the data store, Toksos teaches optimizing a usage of storage resources in the computerized system to save storage space by automatically deleting one or more of the data items from the data store, as described above, therefore, one of ordinary skill in the art would have recognized that the combination of Miller and Toksos would have yield predictable results in saving storage space in computer system.
Response to Arguments/Amendment
7. Applicant's arguments with respect to claims 1-26 have been fully considered but are not persuasive.
I. Claim Rejections - 35 USC § 101
Claims 1-26 are rejected under 35 U.S.C. 101 because the claim invention is directed to a judicial exception (i.e., law of nature, natural phenomenon, or abstract idea) without significantly more (See details above).
In response to the Applicant’s arguments that the claims do not recite an abstract idea, the Examiner respectfully disagrees and submits that the claims recite the method for predicting the necessity of screen recording for each agent. The method involves contact centers monitor interactions between agents and customers from evaluation purposes and follow up actions, such as coaching plans, agents’ performance improvement. The monitoring for evaluation of the agents’ performance during interactions maybe based on calls recording and screen recordings of events that took place during the interaction (see Specification at least para [0001]). The claim recites the steps: for one or more the interactions, calculating an evaluation likelihood value, for one or more of the remote computing devices, calculating a recording percentage, based on one or more of the calculate like likelihood value and the calculated recording percentages, optimizing a usage of storage resources comprising: performing at least one of: recoding one or more data items from one or more interactions and deleting one or more data items, under its broadest reasonable interpretation when read in light of the Specification, falls within “Certain Methods of Organizing Human Activity” grouping of abstract ideas as they cover performance of managing personal behavior or relationships or interactions between people including social activities, teaching, and following rules or instructions.
The claims recite the steps of: for one or more the interactions, calculating an evaluation likelihood value, for one or more of the remote computing devices, calculating a recording percentage, based on one or more of the calculate like likelihood value and the calculated recording percentages, optimizing a usage of storage resources comprising: performing at least one of: recoding one or more data items from one or more interactions and deleting one or more data items, as drafted, is a process that, under its broadest reasonable interpretation when read in light of the Specification, covers performance of the limitations in the mind, can be practically performed by human in their mind or with pen/paper, but for the recitation of generic computer components. That is, other than reciting “a computer/processor/automatically”, nothing in the claim elements preclude the steps from practically being performed in the mind. The mere nominal recitation of generic computing devices does not take the claim limitation out of the Mental Processes grouping of abstract ideas. Thus, if a claim limitation, under its broadest reasonable interpretation, covers performance of the limitation in the mind, then it falls within the “Mental Processes” grouping of abstract ideas (concepts performed in the human mind including an observation, evaluation, judgment, opinion). See MPEP 2106.04(a)(2), subsection III. Therefore, the claims recite an abstract idea.
In response to the Applicant’s arguments that Claim 1 is directed to more efficient storage of computer data, e.g., “performing at least one of: recording one or more data items from one or more interactions on one or more of the remote computing devices, and deleting one or more of the data items from the data store”, which amounts to a specific improvement to technology, the Examiner respectfully disagrees and submits that the limitations “performing at least one of: recording one or more data items from one or more interactions on one or more of the remote computing devices, and deleting one or more of the data items from the data store” are recited as being performed by the processor, which is recited at a high level of generality and are used to perform an abstract idea, as discussed above in Step 2A, Prong One, such that it amounts to no more than mere instructions to apply the exception using a generic computer. Accordingly, the additional elements evaluated individually and in combination do not integrate the abstract idea into a practical application because they comprise or include limitations that are not indicative of integration into a practical application such as adding the words "apply it" (or an equivalent) with the judicial exception, or mere instructions to implement an abstract idea on a computer, or merely uses a computer as a tool to perform an abstract idea -- See MPEP 2106.05(f).
While the disclosure states at least that “some embodiments may use intelligent optimization of storage usage and solve various difficulties associated with limited storage resources in the context of large-scale data collection and maintenance” (para. [0025]), and Applicant also states the currently claimed invention provides the strictly technological improvement of optimizing the usage of storage resources for storing specific computerized data items in a computing system - and it does so by performing strictly technological operations on these data items to produce tangible technological outputs on computer hardware (e.g., recording or deleting data items), which would be virtually impossible to achieve by non- technological means, the Examiner submits that the limitations “performing at least one of: recording one or more data items from one or more interactions on one or more of the remote computing devices, and deleting one or more of the data items from the data store” do not provide any improvements to the technology, improvements to the functioning of the computer, the processor, the memory, or other technology. They do not recite a particular machine or manufacture that is integral to the claims, and do not transform or reduce a particular article to a different state or thing. At best, the claimed combination amounts to an improvement to the abstract idea of predicting the necessity of screen recording for each agent, rather than to any technology. See MPEP 2106.05(a). Thus, even when considering the elements in combination, the claim as a whole does not integrate the recited exception into a practical application.
As discussed in Step 2A, Prong Two above, the recitation of the one or more processors to perform limitations “for one or more the interactions, calculating an evaluation likelihood value, for one or more of the remote computing devices, calculating a recording percentage, based on one or more of the calculate like likelihood value and the calculated recording percentages, optimizing a usage of storage resources comprising: performing at least one of: recoding one or more data items from one or more interactions and deleting one or more data items”, amounts to no more than mere instructions to apply the exception using a generic computer component.
Even when considered in combination, these additional elements represent mere instructions to implement an abstract idea or other exception on a computer and insignificant extra-solution activity, which do not provide an inventive concept. Therefore, the claims are not patent eligible.
Accordingly, the 101 rejection is maintained.
II. Claim Rejections - 35 USC § 103
Applicant's arguments filed regarding to claims 1-26 have been fully considered but they are not persuasive and the new ground of 103 rejection necessitated by the Amendment.
In response to the Applicant’s arguments that Toksos is focused on selecting a recorded video to transmit to a client, and has nothing whatsoever to do with automatically recording one or more data items from one or more interaction on one or more of the remote computing devices, the Examiner respectfully disagrees and submits that Toksos discloses in para [0008], By selecting and presenting the appropriate video for the application, the present disclosure may thus reduce the time of execution of an application by training the user to properly use and run the application, thereby saving client-side power consumption, network bandwidth, and computer processing time. Additionally, by optimizing selection of the recorded video, the server may discard low-performing videos, saving server-side storage space, and allowing other computing devices (e.g. edge caching servers, content redistribution systems, etc.) to cache and retransmit the selected video, reducing server bandwidth consumption; para [0126], the system 200 can automatically generate recordings 110 of the application 238 executing on multiple client devices 110 and then select the most appropriate or optimal recording 110 from the recordings 110 to instruct or train a user at the client device 105 to operate the application 238. As such, the system 200 detailed herein may thus reduce the time of execution of an application 238 by training the user to properly use and run the application 238, thereby saving power consumption, network bandwidth, and computer processing time at the client device 105, data processing system 210, and the network 205; para [0137], The data processing system 210 can receive the interaction log (step 362). The data processing system 210 can then proceed to traverse through the interactions recorded on the interaction log in steps 364-374 to update the performance metric for the video. The data processing system 210 can identify a type of interaction (step 364). The data processing system 210 can identify coordinates of interaction (step 366). The data processing system 210 can identify a time of interaction (step 368). Thus, Toksos’ system automatically records one or more data items from one or more interaction on one or more of the remote computing devices.
In response to the Applicant’s arguments that Miller does not consider a likelihood that an interaction will be evaluated, Miller simply discusses reviewing and analyzing interactions, the Examiner respectfully disagrees and submits that Miller teaches in para [0005] that “systems and methods for automatically evaluating or scoring agent behavior based on analyzing interactions between customers and agents of a contact center and for managing contact center operations in accordance with the automatically computed scores”; para [0037], “the evaluation of the interaction may result in a score that reflects the agent's performance on that interaction. The score may be used, with other scores and/or other performance metrics, to compute one or more aggregate scores that may reflect the agent's overall performance. Individual scores or an aggregate score may be used to indicate that an agent has met, exceeded, or failed to meet particular standards of performance”; para [0039], “The automatically computed scores may be used to classify the interactions as being uninteresting or interesting. Furthermore, the computed scores may be used to control a further action taken on the recorded interaction, such as assigning the interesting interactions to one or more human supervisors for manual evaluation, deleting uninteresting interactions, and preserving interesting interactions for further use; para [0060], “The contact center system may further include a quality management server 170 configured to provide quality monitoring of agents of the contact center. The quality management server 162 may provide a user interface for human supervisors to evaluate agents, such as by reviewing one or more recorded interactions associated with an agent (or providing real-time review of an agent while the agent is participating in the interaction), an automatic evaluation module for automatically analyzing an interaction, and an action module for generating actions in response to computing particular scores associated with an interaction or interactions, where the actions may include deleting the associated interaction (e.g., removing it from the mass storage device 126), setting an expiration date for the recording (e.g., a date on which the recording should be deleted), marking the recording to be preserved); para [0063], “The automatic analysis or automatic evaluation may be performed on metadata associated with the interaction (such as the length of the interaction in minutes and the number of transfers between different agents of the contact center) as well as the content of the interaction (e.g., an analysis of the text transcripts of the interaction to detect keywords or phrases), and the automatic evaluation of the interaction may be used to generate one or more evaluation scores representing the agent's performance during the interaction.” Thus, in Miller’s, the automatically computed scores may be used to classify the interactions as being uninteresting or interesting (a likelihood that an interaction will be evaluated) and the computed scores may be used to control a further action taken on the recorded interaction. Therefore, Miller teaches a likelihood that an interaction will be evaluated as claimed.
In response to the Applicant’s arguments that Miller does not teach calculating an evaluation likelihood value calculated based on historical data elements indicating whether a past interaction was chosen for evaluation, the Examiner respectfully disagrees and submits that Miller teaches in para [0091], the one or more prediction models are trained by a model training module 175 based on historical recorded interactions and the scores Y assigned to those interactions by human supervisors; para [0093], When generating training data for training the prediction module, the manually generated score or scores for a given interaction is retrieved from the quality service 176, and the corresponding historical interaction is retrieved from the mass storage device 126 and supplied to the interaction feature extractor 174 to extract the features for the historical interaction. The combinations of historical interaction features and their associated evaluations represent individual data points of a set of training data. For example, where the evaluation may include multiple scores (e.g., m scores), then the set of scores may be represented as the vector (Y.sub.1, Y.sub.2, . . . , Y.sub.m), and the combination of the feature vector of a historical interaction (the historical interaction features) and its associated scores may be represented as {(X.sub.1, X.sub.2, . . . , X.sub.n), (Y.sub.1, Y.sub.2, . . . , Y.sub.m)}. para [0120], the condition may be based on using a particular agent's past performance predict an agent's current performance. The controller may then compare the predicted performance with the automatically evaluated performance on a recent interaction with to detect deviations in the agent's performance from history, which may show signs of worsening performance (and resulting in the automatic scheduling of additional training) or improving performance (where the agent may be sent a message congratulating him or her on the improvement and encouraging continued progress)). Thus, in Miller’s, the scores are calculated based on historical recorded interactions and indicating whether a past interaction was chosen for evaluation (para [0093], The controller may then compare the predicted performance with the automatically evaluated performance on a recent interaction with to detect deviations in the agent's performance from history). Therefore, Miller teaches calculating an evaluation likelihood value calculated based on historical data elements indicating whether a past interaction was chosen for evaluation as claimed.
Accordingly, the 103 rejection is maintained.
Conclusion
8. 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.
9. Claims 1-26 are rejected.
10. Any inquiry concerning this communication or earlier communications from the examiner should be directed to examiner NGA B NGUYEN whose telephone number is (571) 272-6796. The examiner can normally be reached on Monday-Friday 7AM-5PM.
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If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, Beth Boswell can be reached on (571) 272-6737. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
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/NGA B NGUYEN/Primary Examiner, Art Unit 3625 April 4, 2026