DETAILED ACTION
This communication is in response to the Amendments and Arguments filed on March 17, 2026. Claims 1, 2, 4, 6-16, and 18-23 are pending and have been examined. Hence, this action has been made FINAL.
Notice of Pre-AIA or AIA Status
The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA .
Response to Arguments
The reply filed on March 17, 2026 has been entered. Applicant’s arguments with respect to claims 1, 2, 4, 6-16, and 18-23 have been considered but are not persuasive/moot in view of new ground(s) of rejection caused by the amendments.
With respect to the applicant’s arguments to claim rejections under 35 U.S.C § 101, Applicant has amended each of the independent claims and asserts that “As in Ex parte Desjardins, the embodiments claimed in the present application do in fact improve the functioning of a computer.” The examiner respectfully disagrees with these assertions. The Appeals Review Panel (ARP) decision highlights in Ex Parte Desjardins pg. 9, paragraph 1 that “When evaluating the claim as a whole, we discern at least the following limitation of independent claim 1 that reflects the improvement: "adjust the first values of the plurality of parameters to optimize performance of the machine learning model on the second machine learning task while protecting performance of the machine learning model on the first machine learning task." We are persuaded an improvement to how the machine learning model itself operates, and not, for example, the identified mathematical calculation." In other words, an improvement must be made to the machine learning model itself in order to constitute integration into practical application. Simply using a “machine learning model” to fulfill a purpose without improving the machine learning model itself merely equates to “apply it” or otherwise merely uses a generic computer as a tool to perform an abstract which are not indicative of integration into a practical application as per MPEP 2106.05(f). As amended, there is no language in the independent claims that provides an improvement to a machine learning model itself.
Applicant further asserts that “The claimed invention reflects an improvement in the technical field of processing data, particularly unstructured data.” The examiner respectfully disagrees with these assertions. As per MPEP 2106.05(a), “it is important to keep in mind that an improvement in the abstract idea itself (e.g. a recited fundamental economic concept) is not an improvement in technology. For example, in Trading Technologies Int’l v. IBG, 921 F.3d 1084, 1093-94, 2019 USPQ2d 138290 (Fed. Cir. 2019), the court determined that the claimed user interface simply provided a trader with more information to facilitate market trades, which improved the business process of market trading but did not improve computers or technology.” Accordingly, an improvement in analyzing and summarizing unstructured data is analogous to an improvement in an abstract idea, and thus cannot be considered an improvement in computer technology itself as per the above MPEP section.
Applicant further asserts that “The claimed limitations also materially improve compute performance by removing any redundant modifications from being performed, thereby reducing computational overhead of the system. For example, paragraph 0093 explains that "received modifications may be stored in a queue and performed in a same order as they were received. In other approaches, redundant modifications may be deduplicated before being used to alter the tags and/or clusters of information. This desirably reduces performance overhead while also lowering data errors resulting from invalid (e.g., outdated) data" (emphasis added).” The examiner respectfully disagrees with these assertions. As per MPEP 2106.05(a)(I), “It is important to note that in order for a method claim to improve computer functionality, the broadest reasonable interpretation of the claim must be limited to computer implementation. That is, a claim whose entire scope can be performed mentally, cannot be said to improve computer technology. Synopsys, Inc. v. Mentor Graphics Corp., 839 F.3d 1138, 120 USPQ2d 1473 (Fed. Cir. 2016) (a method of translating a logic circuit into a hardware component description of a logic circuit was found to be ineligible because the method did not employ a computer and a skilled artisan could perform all the steps mentally). Similarly, a claimed process covering embodiments that can be performed on a computer, as well as embodiments that can be practiced verbally or with a telephone, cannot improve computer technology. See RecogniCorp, LLC v. Nintendo Co., 855 F.3d 1322, 1328, 122 USPQ2d 1377, 1381 (Fed. Cir. 2017) (process for encoding/decoding facial data using image codes assigned to particular facial features held ineligible because the process did not require a computer).” Accordingly, the step of removing duplicate tags from a set of tags can be performed entirely mentally, and thus cannot be said to improve computer technology. As amended, there is no language in the independent claims that would prevent a human from performing these steps, as addressed in further detail below with respect to claim rejections under 35 USC § 101.
With respect to the applicant’s arguments to claim rejections under 35 U.S.C § 103, the applicant’s arguments with respect to claims 1, 2, 4, 6-16, and 18-23 have been considered but are moot in view of new ground(s) of rejection caused by the amendments.
Claim Interpretation
The claims in this application are given their broadest reasonable interpretation using the plain meaning of the claim language in light of the specification as it would be understood by one of ordinary skill in the art. The broadest reasonable interpretation of a claim element (also commonly referred to as a claim limitation) is limited by the description in the specification.
The following terms in the claims have been given the following interpretations in light of the specification:
Journey tag: paragraph [0084], “journey tags may be used to provide information at the location of a respondent along the path of completing a task. In other words, journey tags may indicate which stage of completing the task the received data corresponds to. In some approaches, journey tags may be used identify common paths or deviations in data and develop an overall understanding of the data. For example, before a study is conducted, a researcher may predefine the steps along a path of completing a task, and tags can be generated for each of the predefined steps accordingly.”
Thus, a journey tag is any tag that provides time-based or step-wise information for a given process or task. This definition is used for purposes of searching for prior art, but cannot be incorporated into the claims.
Workflow tag: paragraph [0086], “It follows that "workflow tags" are tags that help with task management. For instance, a researcher may want to know the specific moment when a participant was able to define a profile, complete a purchase, or find a product. Workflow tags are able to align this task with other performance metrics, e.g., such as time on task and task completion. A workflow may be captured in one user session which is recorded for analysis. Workflow tags are thereby used to define the transition of phases in the workflow and the model used for predicting such tags will be hybrid in nature.”
Thus, a workflow tag is any tag that provides time-based, task-based, transition-based, or user-behavior-based information. This definition is used for purposes of searching for prior art, but cannot be incorporated into the claims.
Content clarifier module: paragraph [0053], “The processor 212 is also shown as including a content clarifier module 208 configured to evaluate the received data and dynamically simplify the content in the received data. In other words, the content clarifier module 208 may be used to evaluate the content of the received data and generate a simplified summary or meaning.”
Thus, a content clarifier module is any component of a system that evaluates and simplifies received data. This definition is used for purposes of searching for prior art, but cannot be incorporated into the claims.
Should applicant wish different definitions, Applicant should point to the portions of the specification that clearly show a different definition.
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, 2, 4, 6-16, and 18-23 are rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more. All of the claims are method claims (1-9, 22), apparatus/machine claims (10-21) or manufacture claim under (Step 1), but under Step 2A all of these claims recite abstract ideas and specifically mental processes. These mental processes are more particularly recited in claims 1, 10, and 19 as:
summarizing content in the received recording data…
evaluating the received recording data…
dynamically generating a simplified version of the received recording data…
using a machine learning model to analyze the simplified version of the received recording data…
dividing the received recording data into clusters of information…
generating tags for the clusters of information…
deduplicating the received modifications…
using the deduplicated modifications to finalize the generated tags and/or clusters of information…
converting the finalized tags into a predetermined exportable format…
sending the finalized tags to a target location over a network…
Under Step 2A Prong One, claims 1, 10, and 19 are directed to an abstract idea and specifically a mental process. As detailed above, the steps of summarizing, analyzing, dividing, etc. may be practically performed in the human mind with the use of a physical aid such as a pen and paper. For example, a human could receive a list of predefined category tags alongside a speech recording of a user, segment the speech into sentences, assign the set of sentences into clusters by analyzing each sentence, write a detailed description for each cluster, combine the descriptions and the category tags to produce labels for each cluster, and then show the labels to a second human. The human can then receive modification suggestions from the second human, deduplicate the received modifications, modify the tags according to the modifications to finalize the cluster labels, convert the finalized cluster labels into JSON format, and then send the finalized JSON to the office building next door.
Under Step 2A Prong Two, this judicial exception is not integrated into a practical application because claims 1, 2, 4, 6-21 and 22 do not recite additional elements that integrate the exception into a practical application. In particular, claims 1, 10, 19 recite the additional elements of a computer readable storage medium (¶ [0024]), a processor (¶ [0027]) a machine learning model (¶ [0049]), and a network (¶ [0047]). These additional elements are recited at a high level of generality and merely equate to “apply it” or otherwise merely uses a generic computer as a tool to perform an abstract which are not indicative of integration into a practical application as per MPEP 2106.05(f). Further, claims 1, 10, and 19 recite the additional elements of “receiving…”, which amounts to insignificant extra-solution activities which are not indicative of integration into a practical application as per MPEP 2106.05(g). 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.
Under Step 2B, the claims do not recite additional elements that are sufficient to amount to significantly more than the judicial exception. As discussed above with respect to the integration of the abstract idea into a practical application, the additional elements of using a computer is noted as a general computer {computer readable storage medium (¶ [0024]); processor (¶ [0027]); machine learning model (¶ [0049]); network (¶ [0047])}. Mere instructions to apply an exception using a generic computer component cannot provide an inventive concept. Further, the additional limitations in the claims noted above are directed towards insignificant extra-solution activities. The claims are not patent eligible.
With respect to claims 2 and 11, the claim relates to receiving and implementing tag modifications from a user. This relates to a human showing a second human the generated tags, receiving feedback from the second human, implementing the feedback, and then presenting the modified tags to the second human. No additional limitations are present. The claim does not include additional elements that are sufficient to amount to significantly more than the judicial exception.
With respect to claims 4 and 13, the claim relates to analyzing background information and generating tags based off of the background information. This relates to a human receiving context for the received recording data, analyzing it, and then creating new tags based off of the context and analysis. The additional limitation of a “machine learning model” is recited at a high level of generality and merely equate to “apply it” or otherwise merely uses a generic computer as a tool to perform an abstract which are not indicative of integration into a practical application as per MPEP 2106.05(f). The claim does not include additional elements that are sufficient to amount to significantly more than the judicial exception.
With respect to claims 6 and 15, the claim relates to sending the recording data to a content clarifier module, which then summarizes the recording data. This relates to a human reciting the memorized speech to a domain expert with instructions to simplify the speech, and then receiving back the simplified data from the expert. No additional limitations are present. The claim does not include additional elements that are sufficient to amount to significantly more than the judicial exception.
With respect to claims 7 and 16, the claim relates to generating tags under a type consisting journey tags and/or workflow tags. This relates to a human using journey tags and/or workflow tags to tag their documents. No additional limitations are present. The claim does not include additional elements that are sufficient to amount to significantly more than the judicial exception.
With respect to claims 8 and 20, the claim relates to received recording data taking the form of one of video recordings, audio recordings, and screen recordings. The limitation of “receiving…” is directed towards insignificant extra-solution activities which are not indicative of integration into a practical application as per MPEP 2106.05(g). No additional limitations are present. The claim does not include additional elements that are sufficient to amount to significantly more than the judicial exception.
With respect to claims 9 and 18, the claim relates to the machine learning model being configured to perform NLP and/or classification machine learning. This is additional element is recited at a high level of generality and merely equates to “apply it” or otherwise merely uses a generic computer as a tool to perform an abstract which are not indicative of integration into a practical application as per MPEP 2106.05(f). No additional limitations are present. The claim does not include additional elements that are sufficient to amount to significantly more than the judicial exception.
With respect to claim 12, the claim relates to converting the modified tags to a specific format compatible with an operating system. This relates to a human formatting the modified tags to be compatible with the tagging infrastructure of a particular database. No additional limitations are present. The claim does not include additional elements that are sufficient to amount to significantly more than the judicial exception.
With respect to claims 14, the claim relates to generating modified tags based off of the predetermined tags, details from the machine learning model, and the inductive tags. This relates to a human using the contextual tags alongside the written description and pre-labelled tags to create new tags. No additional limitations are present. The claim does not include additional elements that are sufficient to amount to significantly more than the judicial exception.
With respect to claim 21, the claim relates to retraining a machine learning model using clusters and generated tags. This relates to a human improving their understanding of clustering by performing it repeatedly. The additional limitation of a “machine learning model” is recited at a high level of generality and merely equate to “apply it” or otherwise merely uses a generic computer as a tool to perform an abstract which are not indicative of integration into a practical application as per MPEP 2106.05(f). Further, the additional limitation of “to more accurately analyze source data as well as dynamically generate clusters and corresponding tags to support cognitive categorization and analysis of the source data” recites no structural language, thus amounting to intended use which cannot be considered patentable. The claim does not include additional elements that are sufficient to amount to significantly more than the judicial exception.
With respect to claim 22, the claim relates to the machine learning model being trained to evaluate unstructured data in real time and develop tagged clusters of the unstructured data. This relates to a human reading and categorizing unstructured texts. The additional limitation of a “machine learning model” is recited at a high level of generality and merely equate to “apply it” or otherwise merely uses a generic computer as a tool to perform an abstract which are not indicative of integration into a practical application as per MPEP 2106.05(f). No additional limitations are present. The claim does not include additional elements that are sufficient to amount to significantly more than the judicial exception.
With respect to claim 23, the claim relates to dynamically updating labels based on updated recording data and clusters. This relates to a human recreating their clusters and associated cluster labels after receiving a subsequent duration of speech. No additional limitations are present. The claim does not include additional elements that are sufficient to amount to significantly more than the judicial exception.
For all of the above reasons, taken alone or in combination, claims 1, 2, 4, 6-16, and 18-22 recite a non-statutory mental process.
Claim Rejections - 35 USC § 103
The following is a quotation of 35 U.S.C. 103 which forms the basis for all obviousness rejections set forth in this Office action:
A patent for a claimed invention may not be obtained, notwithstanding that the claimed invention is not identically disclosed as set forth in section 102, if the differences between the claimed invention and the prior art are such that the claimed invention as a whole would have been obvious before the effective filing date of the claimed invention to a person having ordinary skill in the art to which the claimed invention pertains. Patentability shall not be negated by the manner in which the invention was made.
Claims 1, 2, 6, 8-12, 15, 18, 21, and 22 are rejected under 35 U.S.C. 103 as obvious over US Patent Publication 20170076225 A1 (Zhang et al.) in view of US Patent Publication 20210027896 A1 (Eun et al.) in view of US Patent Publication 20150074110 A1 (Paek et al.) in view of US Patent Publication 20180014053 A1 (Venkatraman et al.).
Claim 1
Regarding claim 1, Zhang et al. disclose a computer-implemented method, comprising:
receiving [recording] data and predetermined tags associated with the received [recording] data (Zhang et al. ¶ [0029], "classification system 132 may use validated training data that includes a set of content items tagged with dimensions 116 by domain experts to train one or more statistical models to classify the content items by dimensions 116."; ¶ [0032], "As mentioned above, the classification system may be used to classify content items 216 from content repository 134 with respect to a number of dimensions, such as dimensions 116 of FIG. 1." Dimensions 116 are considered analogous to the claimed predetermined tags.); …
using a machine learning model to analyze the [simplified version of the] received [recording] data (Zhang et al. ¶ [0041], "For example, analysis apparatus 202 and/or another component of the classification system may obtain content items 216 from content repository 134 and generate a set of features (e.g., features 1 218, features n 220) from each of the content items.");
dividing the received recording data into clusters of information (Zhang et al. ¶ [0030], "A reporting system 140 may then output one or more groupings 144 of the content items by topics 114 and/or classification tags 120.");
generating tags for the clusters of information by merging (i) the predetermined tags, (Zhang et al. ¶ [0028], " Next, a classification system 132 may generate a set of classification tags 120 for the content items based on a set of dimensions 116. Dimensions 116 may represent categories or classes by which the content items are to be classified.") and (ii) details produced by the machine learning model (Zhang et al. ¶ [0041], "For example, analysis apparatus 202 and/or another component of the classification system may obtain content items 216 from content repository 134 and generate a set of features (e.g., features 1 218, features n 220) from each of the content items. Analysis apparatus 202 may then provide the features for each content item as input to statistical model 206, and statistical model 206 may output one or more classification tags for the content item based on the inputted features." Generated features from content items are considered analogous to the claimed details produced by machine learning.), [wherein the generated tags include journey tags and/or workflow tags, wherein the journey tags and/or workflow tags are inductive tags that are configured to be generated dynamically based at least in part on the clusters of information];
receiving modifications to the generated tags from a user (Zhang et al. ¶ [0047], "Management apparatus 204 may additionally obtain one or more validated tags (e.g., validated tag 1 226, validated tag o 228) for content items 216 from the users. The validated tags may represent corrections to and/or verifications of classification tags for content items 216 from statistical model 206."); … and
using the [deduplicated] modifications to finalize the generated tags and/or clusters of information (Zhang et al. ¶ [0063]-[0064], "the validated training data is used to produce a statistical model for classifying content using the set of dimensions represented by the first set of classification tags (operation 404). ... The statistical model is then used to generate a second set of classification tags for a second set of content items (operation 406)").
Zhang et al. do not explicitly disclose all of simplifying data.
However, Eun et al. disclose receiving [recording] data (Eun et al. ¶ [0051], "The medical-event embedding engine 240 may be provided with medical events such as two or more time-separated medical events in the medical records in medical records database 120 for a cohort of patients.") [and predetermined tags associated with the received recording data];
using a machine learning model to summarize content in the received [recording] data (Eun et al. ¶ [0052], "The medical-event embedding engine 240 may be trained to generate vector representations of medical events in a continuous vector space, using training data 247 stored in memory 232 and/or medical records data from medical records database 120. " Vector reduction is considered analogous to summarizing content) by: evaluating the received [recording] data (Eun et al. ¶ [0051], "data preparation engine 244 may modify the medical records for the pre-identified cohort, before the medical events of the medical records are provided to medical-event embedding engine 240."), and dynamically generating a simplified version of the received [recording] data (Eun et al. ¶ [0062], "Mapping server 130 may then (e.g., by operation of medical-event embedding engine 240) perform a medical-event embedding operation 404 to generate vectors associated with medical events in the medical records"), wherein the simplified version of the received [recording] data includes less data than the received [recording] data (Eun et al. ¶ [0062], "In some operational scenarios, mapping server 130 may perform one or more dimensionality reduction operations on the single vector representations." Dimensionality reduction is considered analogous to simplification);
using the machine learning model (Eun et al. ¶ [0050], "each of medical-event embedding engine 240, clustering engine 246, and cluster profiling engine 248 may include a machine-learning model that implements a neural network.") to analyze the simplified version of the received [recording] data (Eun et al. ¶ [0063], "Mapping server 130 may then (e.g., by operation of clustering engine 246 on the single vector representations) perform a clustering operation 406 to identify one or more clusters of the patients 306 in the cohort 304 that have similar patient journeys 308.");
dividing the received [recording] data into clusters of information (Eun et al. ¶ [0063], "Mapping server 130 may then (e.g., by operation of clustering engine 246 on the single vector representations) perform a clustering operation 406 to identify one or more clusters of the patients 306 in the cohort 304 that have similar patient journeys 308."); and
generating tags for the clusters of information (Eun et al. ¶ [0063], " the clustering operation 406 can include selecting a subset (e.g., one third or another fraction) of the patient cohort 304 and generating (e.g., with clustering engine 246) clusters and associated cluster labels for the subset.") [by merging (i) the predetermined tags, and (ii) details produced by the machine learning model], wherein the generated tags include journey tags and/or workflow tags (Eun et al. ¶ [0063], "Mapping server 130 may then (e.g., by operation of clustering engine 246 on the single vector representations) perform a clustering operation 406 to identify one or more clusters of the patients 306 in the cohort 304 that have similar patient journeys 308. ... the clustering operation 406 can include selecting a subset (e.g., one third or another fraction) of the patient cohort 304 and generating (e.g., with clustering engine 246) clusters and associated cluster labels for the subset." Cluster labels associated with patients' medical journeys are considered analogous to journey tags ), wherein the generated tags include journey tags and/or workflow tags (Eun et al. ¶ [0063], "Mapping server 130 may then (e.g., by operation of clustering engine 246 on the single vector representations) perform a clustering operation 406 to identify one or more clusters of the patients 306 in the cohort 304 that have similar patient journeys 308." ¶ [0079], "At block 608, a cluster profiling engine such as cluster profiling engine 248 of FIG. 2 of the mapping server identifies differentiating medical events (see, e.g., the differentiating medical events described in connection with FIG. 5 as an example) of each of the identified clusters by performing a cluster profiling operation using an output of the clustering engine (e.g., the cluster labels...) and the medical records of the patients in the identified clusters" Cluster labels associated with patients' medical journeys are considered analogous to journey tags. See Figure 5 for examples of journey tags), wherein the journey tags and/or workflow tags are inductive tags that are configured to be generated dynamically based at least in part on the clusters of information (Eun et al. ¶ [0063]-[0069], "It should be appreciated that cluster profiling engine 248 identifies the distinguishing features of each cluster, after the clusters are identified, rather than the clusters being forced to conform to pre-determined cluster labels." Distinguishing features are induced from generated clusters. Therefore, cluster labels are considered analogous to inductive tags)….
It would have been obvious to a person having ordinary skill in the art before the time of the effective filing date of the claimed invention of the instant application to modify Zhang et al.’s tagging method to include Eun et al.’s tagging method because such a modification is the result of combining prior art elements according to known methods to yield predictable results. More specifically, Zhang et al.’s tagging method as modified by Eun et al.’s simplification can yield a predictable result of reducing system overhead since simpler data representations necessitate fewer mathematical operations. Thus, a person of ordinary skill would have appreciated including in Zhang et al.’s tagging method the ability to do Eun et al.’s simplification since the claimed invention is merely a combination of old elements, and in the combination each element merely would have performed the same function as it did separately, and one of ordinary skill in the art would have recognized that the results of the combination were predictable.
Zhang et al. in view of Eun et al. do not explicitly disclose all of converting and exporting tags to a target location over a network.
However, Paek et al. disclose converting the finalized tags into a predetermined exportable format (Paek et al. ¶ [0164], "Further, the transceiver 120 can receive the conversion information with the URL of contents from the tag type converting unit 230 of the COT client 200 and can change the data format of the tag information corresponding to the URL of contents to be the same as in the data format conversion process according to the conversion information of the tag information request unit 220 on the basis of the conversion information, and transmit it."); and
sending the finalized tags to a target location over a network (Paek et al. ¶ [0165], "Further, when both of the URL of contents and tag information are received, the controller transmit them to the tag information storage 160 and the tag information storage 160 can match the URL of contents and the tag information with each other and store in on the tag DB 110.").
It would have been obvious to a person having ordinary skill in the art before the time of the effective filing date of the claimed invention of the instant application to modify Zhang et al. in view of Eun et al. to include Paek et al.’s tag conversion and export system because such a modification is the result of combining prior art elements according to known methods to yield predictable results. More specifically, Zhang et al. in view of Eun et al. as modified by Paek et al.’s tag conversion and export system can yield a predictable result of improving user experience since retaining modified tag information in a database would eliminate the need for users to repeatedly correct tags. Thus, a person of ordinary skill would have appreciated including in Zhang et al. in view of Eun et al. the ability to do Paek et al.’s tag conversion and export system since the claimed invention is merely a combination of old elements, and in the combination each element merely would have performed the same function as it did separately, and one of ordinary skill in the art would have recognized that the results of the combination were predictable.
Zhang et al. in view of Eun et al. in view of Paek et al. do not explicitly disclose all of deduplicating tags.
However, Venkatraman et al. disclose receiving recording data and predetermined tags associated with the received recording data (Venkatraman et al. ¶ [0051], "The video recommendation system 114 is configured to receive the set of preference data and fetch the one or more tagged videos associated with the set of preference data of the user 102a from the second database 118." Tagged videos are considered analogous to predetermined tags associated with received recording data); and
deduplication (Venkatraman et al. ¶ [0070]-[0071], "semantic context information may be used to automatically tag, fragment, cluster or assemble videos on demand. ... The video recommendation system 114 removes duplicate tags from set of tags of the real time and dynamically assembled video in the temporary memory of the main server 112.").
It would have been obvious to a person having ordinary skill in the art before the time of the effective filing date of the claimed invention of the instant application to modify Zhang et al. in view of Eun et al. in view of Paek et al. to incorporate Venkatraman et al.’s deduplication technique.
The suggestion/motivation for doing so would have been that, “The duplicate tags … are flushed in the disk for faster transmission and caching of the assembled video on different communication devices,” as noted by the Venkatraman et al. disclosure in paragraph [0071].
Claim 2
Regarding claim 2, the rejection of claim 1 is incorporated.
Zhang et al. further disclose displaying the generated tags to the user (Zhang et al. ¶ [0046], "management apparatus 204 may output one or more groupings 144 of content items 216 by the classification tags. For example, management apparatus 204 may display groupings 144 within a GUI of a reporting system, such as reporting system 140 of FIG. 1. Within the GUI, users may sort, filter, and/or search for content items 216 based on the classification tags, topics, keywords, and/or other attributes of content items 216.");
receiving the modifications to the generated tags from the user (Zhang et al. ¶ [0047], "Management apparatus 204 may additionally obtain one or more validated tags (e.g., validated tag 1 226, validated tag o 228) for content items 216 from the users. The validated tags may represent corrections to and/or verifications of classification tags for content items 216 from statistical model 206."); and
applying the received modifications to the generated tags (Zhang et al. ¶ [0063]-[0064], " the validated training data is used to produce a statistical model for classifying content using the set of dimensions represented by the first set of classification tags (operation 404). ... The statistical model is then used to generate a second set of classification tags for a second set of content items (operation 406)"), wherein outputting the finalized tags includes:
exporting the finalized tags (Zhang et al. ¶ [0066], "groupings of the second and third sets of content items by the classification tags may be displayed within a GUI, along with user-interface elements for sorting, filtering, and/or searching the grouped content items by additional keywords, filters, attributes, and/or dimensions."), [wherein the predetermined exportable format is compatible with an operating system implemented at the target location].
Paek et al. further disclose wherein the predetermined exportable format is compatible with an operating system implemented at the target location (Paek et al. ¶ [0164], "Further, the transceiver 120 can receive the conversion information with the URL of contents from the tag type converting unit 230 of the COT client 200 and can change the data format of the tag information corresponding to the URL of contents to be the same as in the data format conversion process according to the conversion information of the tag information request unit 220 on the basis of the conversion information, and transmit it." ¶ [0143], "The configuration of the COT client 200, in detail, may include an editor unit 210, a tag information request unit 220, a tag type converting unit 230, and an authority managing unit 240." Converting tag types to be compatible with COT client 200 by way of component 220's conversion information is considered analogous to a predetermined exportable format being compatible with an operating system).
Claim 6
Regarding claim 6, the rejection of claim 1 is incorporated.
Eun et al. further disclose wherein summarizing content in the received recording data includes:
sending one or more instructions to a content clarifier module (Eun et al. ¶ [0038], "Functions performed by audio-visual media synopsis module 400 and called modules may comprise the following: ... call transcription summarization module 600 to generate a transcript summary and a video summary from the transcription of the audio track and from the audio-visual media" Transcription summarization module 600 is considered analogous to a content clarifier module.) to evaluate the received recording data (Eun et al. ¶ [0084], "At block 620, transcription summarization module 600 may output sentence meaning clusters. Semantic meaning of sentences within each cluster may be similar; semantic meaning of sentences between clusters may be different. ... Sentences in the sentence meaning clusters may be linked to corresponding portions of video 305." The transcript is an intermediary format used to evaluate the received audio-visual media data as a whole. Therefore, clustering the sentences of a transcript of a video is considered analogous to evaluating recording data.) and dynamically simplify the content in the received recording data (Eun et al. ¶ [0088], "At block 630, transcription summarization module 600 may output transcript summary 340 comprising, for example, sentences selected at block 625 as well as video summary 345, wherein video summary comprises portions of video 305 corresponding to transcript summary 340. In this way, transcription summarization module 600 prepares transcript summary 340 and video summary 345 as extractive summaries, comprising quotes or portions of video 305 extracted from video 305, rather than an abstractive or paraphrased summary (which may re-state text in alternative words)."); and
receiving the summarized content from the content clarifier module (Eun et al. ¶ [0088]-[0089], "At block 630, transcription summarization module 600 may output transcript summary 340 comprising, for example, sentences selected at block 625 as well as video summary 345, wherein video summary comprises portions of video 305 corresponding to transcript summary 340. ... At done or return block 699, transcription summarization module 600 may conclude or return to a module or another process which may have called it.").
Claim 8
Regarding claim 8, the rejection of claim 1 is incorporated.
Venkatraman et al. further disclose wherein the received recording data includes one or more types of information selected from the group consisting of: video recordings, audio recordings, and screen recordings (Venkatraman et al. ¶ [0051], "The video recommendation system 114 is configured to ... fetch the one or more tagged videos associated with the set of preference data of the user 102a from the second database 118.").
Claim 9
Regarding claim 9, the rejection of claim 1 is incorporated.
Zhang et al. further disclose wherein the machine learning model is configured to perform natural language processing and/or classification machine learning (Zhang et al. ¶ [0050], "Second, the functionality of statistical model 206 may be implemented using different techniques. In particular, classification tags for content items 216 may be generated using an artificial neural network, naïve Bayes classifier, Bayesian network, clustering technique, logistic regression technique, decision tree, and/or other type of machine learning model or technique." See Figure 2, which displays the statistical model as a subcomponent of the analysis apparatus.), [wherein the machine learning model is a content clarifier module].
Eun et al. further disclose wherein the machine learning model is configured to perform natural language processing and/or classification machine learning (Eun et al. ¶ [0050], "In one or more implementations, each of medical-event embedding engine 240, clustering engine 246, and cluster profiling engine 248 may include a machine-learning model that implements a neural network"), wherein the machine learning model is a content clarifier module (Eun et al. ¶ [0050], "In one or more implementations, each of medical-event embedding engine 240, clustering engine 246, and cluster profiling engine 248 may include a machine-learning model that implements a neural network" Medical-event embedding engine 248 is considered analogous to a content clarifier module; see claim interpretation).
Claim 10
Regarding claim 10, Zhang et al. disclose a computer program product, comprising a computer readable storage medium having program instructions embodied therewith (Zhang et al. ¶ [0017]-[0018], "The data structures and code described in this detailed description are typically stored on a computer-readable storage medium, which may be any device or medium that can store code and/or data for use by a computer system. ... The methods and processes described in the detailed description section can be embodied as code and/or data, which can be stored in a computer-readable storage medium as described above."), the program instructions readable by a processor, executable by the processor, or readable and executable by the processor (Zhang et al. ¶ [0019], "Furthermore, methods and processes described herein can be included in hardware modules or apparatus. These modules or apparatus may include, but are not limited to, an application-specific integrated circuit (ASIC) chip, a field-programmable gate array (FPGA), a dedicated or shared processor that executes a particular software module or a piece of code at a particular time, and/or other programmable-logic devices now known or later developed.").
The remaining limitations of claim 10 are similar in scope to the limitations of claim 1 and therefore are rejected for similar reasons as described above.
Claim 11
Regarding claim 11, the rejection of claim 10 is incorporated. The limitations of claim 11 are similar in scope to that of claim 2 and therefore are rejected for similar reasons as described above.
Claim 12
Regarding claim 12, the rejection of claim 11 is incorporated. The limitations of claim 12 are similar in scope to that of claim 2 and therefore are rejected for similar reasons as described above.
Claim 15
Regarding claim 15, the rejection of claim 10 is incorporated. The limitations of claim 15 are similar in scope to that of claim 6 and therefore are rejected for similar reasons as described above.
Claim 18
Regarding claim 18, the rejection of claim 10 is incorporated. The limitations of claim 18 are similar in scope to that of claim 9 and therefore are rejected for similar reasons as described above.
Claim 21
Regarding claim 21, the rejection of claim 1 is incorporated.
Zhang et al. further disclose using the clusters of information and the generated tags to re-train the machine learning model to more accurately analyze source data as well as dynamically generate clusters and corresponding tags to support cognitive categorization and analysis of the source data (Zhang et al. ¶ [0057]-[0060], "After statistical model 206 is trained, the classification system may use statistical model 206 to generate a second set of relevance tags 304 for the second set of content items 308. … The classification system may also obtain a validated subset 316 of the second set of relevance tags 304 and provide validated subset 316 as additional training data 310 to statistical model 206 ... validated subset 316 may be used to produce an update to statistical model 206, and the update may be used to generate additional relevance tags for additional sets of content items. Consequently, the accuracy of statistical model 206 may be increased by iteratively validating one or more subsets of relevance tags (e.g., second set of relevance tags 304) outputted by statistical model 206 and using the validated subsets as additional training data 310 for statistical model 206.").
Claim 22
Regarding claim 22, the rejection of claim 1 is incorporated.
Zhang et al. further disclose wherein the machine learning model is trained to evaluate unstructured data in real-time as it is received (Zhang et al. ¶ [0032], "the classification system may be used to classify content items 216 from content repository 134 with respect to a number of dimensions, such as dimensions 116 of FIG. 1. As shown in FIG. 2, the classification system includes an analysis apparatus 202 and a management apparatus 204." ¶ [0020], "unstructured data may be included in a set of content items (e.g., content item 1 122, content item y 124). The content items may be obtained from a set of users (e.g., user 1 104, user x 106) of an online professional network 118 or another application or service.") and dynamically develop tagged clusters of the unstructured data (Zhang et al. ¶ [0028], "classification system 132 may generate a set of classification tags 120 for the content items based on a set of dimensions 116." ¶ [0050], "classification tags for content items 216 may be generated using a ... clustering technique").
Claims 4, 13-14, and 19-20 are rejected under 35 U.S.C. 103 as obvious over Zhang et al. in view of Eun et al. in view of Paek et al. in view of Venkatraman et al. as applied to claims 1 and 10 above, and further in view of US Patent 11544292 B1 (Timilsina et al.).
Claim 4
Regarding claim 4, the rejection of claim 1 is incorporated. Zhang et al. in view of Eun et al. in view of Paek et al. in view of Venkatraman et al. disclose all the elements of the claimed invention as stated above.
Zhang et al. further disclose wherein generating tags for the clusters of information includes merging (i) the predetermined tags (Zhang et al. ¶ [0028], "Next, a classification system 132 may generate a set of classification tags 120 for the content items based on a set of dimensions 116. Dimensions 116 may represent categories or classes by which the content items are to be classified."), (ii) the details produced by the machine learning model (Zhang et al. ¶ [0041], "For example, analysis apparatus 202 and/or another component of the classification system may obtain content items 216 from content repository 134 and generate a set of features (e.g., features 1 218, features n 220) from each of the content items." Generated features from content items are considered analogous to the claimed details produced by machine learning.) [, and (iii) the inductive tags] (Zhang et al. ¶ [0041], "Analysis apparatus 202 may then provide the features for each content item as input to statistical model 206, and statistical model 206 may output one or more classification tags for the content item based on the inputted features." Attaching classification tags to inputted features is considered analogous to merging predetermined tags and details produced by a machine learning model).
Zhang et al. in view of Eun et al. in view of Paek et al. in view of Venkatraman et al. do not explicitly disclose all of generating inductive tags based on background information.
However, Timilsina et al. disclose receiving background information corresponding to the received [recording] data (Timilsina et al. ¶ (16), "The data collector 125 may obtain a set of context elements for a data element in a data source (e.g., local data source, data sources 110, etc.). "A set of context elements for a data element is considered analogous to background information corresponding to received data); and
using the machine learning model to:
analyze the received background information, and generate inductive tags based at least in part on the analysis (Timilsina et al. ¶ (17), "The data tagger 130 may evaluate the set of context elements using the machine learning processor 160 to embed one or more tags into the data element. For example, the date and place of purchase of the building supply may be evaluated using a machine learning model to determine that the building supply purchased from the hardware store in November may be used for home improvement. In an example, the one or more tags may include a geolocation tag indicating a location where the data element was generated." Tags generated by evaluating context elements are considered analogous to inductive tags),
wherein generating tags for the clusters of information includes [merging (i) the predetermined tags, (ii) the details produced by the machine learning model] (iii) the inductive tags (Timilsina et al. ¶ (17), "In an example, evaluation of the set of context elements using the machine learning processor may include identification of a task associated with a respective member of the set of context elements and determination of a task tag for the task. The task tag may be included in the one or more tags embedded into the data element." ¶ (40)-(41), “A set of tasks may be determined for a user (e.g., at operation 405). For example, profile data of the user may indicate that the user is performing a home improvement project, will be preparing taxes, and may be preparing to complete a mortgage application. A dataset may be generated including a plurality of data elements associated with the set of tasks (e.g., at operation 410). For example, receipts, income statements, bank statements, employment information, etc. of the user may be included in the dataset.” Generating tags for tasks is considered analogous to the claimed generation of tags for clusters of information.).
It would have been obvious to a person having ordinary skill in the art before the time of the effective filing date of the claimed invention of the instant application to modify Zhang et al. in view of Eun et al. in view of Paek et al. in view of Venkatraman et al. to incorporate the generation of inductive tags as taught by Timilsina et al.
The suggestion/motivation for doing so would have been that, “By determining document classes and tagging the documents based on applicable tasks, the information may be more efficiently retrieved by reducing processing of each document,” as noted by the Timilsina et al. disclosure in paragraph (14).
Claim 13
Regarding claim 13, the rejection of claim 10 is incorporated. The limitations of claim 13 are similar in scope to that of claim 4 and therefore are rejected for similar reasons as described above.
Claim 14
Regarding claim 14, the rejection of claim 13 is incorporated. The limitations of claim 14 are similar in scope to that of claim 4 and therefore are rejected for similar reasons as described above.
Claim 19
Regarding claim 19, the limitations of claim 19 are similar in scope to that of claims 1, 2, and 4, and therefore are rejected for similar reasons as described above.
Claim 20
Regarding claim 20, the rejection of claim 19 is incorporated.
Eun et al. further disclose wherein the received data includes two or more types of information selected from the group consisting of: video recordings, audio recordings, and screen recordings (Venkatraman et al. ¶ [0051], "The video recommendation system 114 is configured to ... fetch the one or more tagged videos associated with the set of preference data of the user 102a from the second database 118." A video recording comprises an audio recording, see Venkatraman et al. ¶ [0047], "each video may be tagged based on speech rendering and analysis."),
wherein the received data is unstructured data (Venkatraman et al. ¶ [0051], "The video recommendation system 114 is configured to ... fetch the one or more tagged videos associated with the set of preference data of the user 102a from the second database 118." A video is considered unstructured data).
Claims 7 and 16 are rejected under 35 U.S.C. 103 as obvious over Zhang et al. in view of Eun et al. in view of Paek et al. in view of Venkatraman et al. as applied to claims 1 and 10 above, and further in view of US Patent Publication 20210097352 A1 (Convolbo).
Claim 7
Regarding claim 7, the rejection of claim 1 is incorporated. Zhang et al. in view of Eun et al. in view of Paek et al. in view of Venkatraman et al. disclose all the elements of the claimed invention as stated above.
Eun et al. further disclose wherein the generated tags include: journey tags (Eun et al. ¶ [0063], "Mapping server 130 may then (e.g., by operation of clustering engine 246 on the single vector representations) perform a clustering operation 406 to identify one or more clusters of the patients 306 in the cohort 304 that have similar patient journeys 308. ... the clustering operation 406 can include selecting a subset (e.g., one third or another fraction) of the patient cohort 304 and generating (e.g., with clustering engine 246) clusters and associated cluster labels for the subset." Cluster labels associated with patients' medical journeys are considered analogous to journey tags) [and workflow tags].
Zhang et al. in view of Eun et al. in view of Paek et al. in view of Venkatraman et al. do not explicitly disclose all of workflow tags
However, Convolbo discloses wherein generated tags include [journey tags and] workflow tags (Karni et al. pg. 432, Section 3.1, Paragraph 2, "Structured tags (“top down”), also termed metadata, are added automatically to the post content captured during process execution. They include terms such as workflow name, business unit, execution date, customer, product and any further structured information residing in information systems that interact with the workflow and that the organization wishes to tag.").
It would have been obvious to a person having ordinary skill in the art before the time of the effective filing date of the claimed invention of the instant application to modify Zhang et al. in view of Eun et al. in view of Paek et al. in view of Venkatraman et al. to include Convolbo’s workflow tags because such a modification is the result of combining prior art elements according to known methods to yield predictable results. More specifically, Eun et al.’s journey tags as modified by Convolbo’s workflow tags can yield a predictable result of improving journey tracking since workflow tags could provide a more detailed workflow analysis for certain events in a patient’s medical journey. Thus, a person of ordinary skill would have appreciated including in Eun et al.’s tagging system the ability to do Convolbo’s workflow tags since the claimed invention is merely a combination of old elements, and in the combination each element merely would have performed the same function as it did separately, and one of ordinary skill in the art would have recognized that the results of the combination were predictable.
Claim 16
Regarding claim 16, the rejection of claim 10 is incorporated. The limitations of claim 16 are similar in scope to that of claim 7 and therefore are rejected for similar reasons as described above.
Claim 23 is rejected under 35 U.S.C. 103 as obvious over Zhang et al. in view of Eun et al. in view of Paek et al. in view of Venkatraman et al. as applied to claim 1 above, and further in view of US Patent Publication 20190026761 A1 (Jain et al.).
Claim 23
Regarding claim 23, the rejection of claim 1 is incorporated. Zhang et al. in view of Eun et al. in view of Paek et al. in view of Venkatraman et al. disclose all the elements of the claimed invention as stated above.
Eun et al. further disclose dynamically [re]generating the journey tags and/or workflow tags (Eun et al. ¶ [0063], "Mapping server 130 may then (e.g., by operation of clustering engine 246 on the single vector representations) perform a clustering operation 406 to identify one or more clusters of the patients 306 in the cohort 304 that have similar patient journeys 308. ... the clustering operation 406 can include selecting a subset (e.g., one third or another fraction) of the patient cohort 304 and generating (e.g., with clustering engine 246) clusters and associated cluster labels for the subset." Cluster labels associated with patients' medical journeys are considered analogous to journey tags)….
Zhang et al. in view of Eun et al. in view of Paek et al. in view of Venkatraman et al. do not explicitly disclose all of regenerating tags in response to receiving updated data.
However, Jain et al. disclose dynamically regenerating [the journey tags and/or workflow] tags (Jain et al. ¶ [0066], "Based on the trend similarity with the labeled samples, the labeling engine 108c can assign labels to the clusters, based on label information of the data according to historical data (which may be present in at least one of the first data repository 103 and the distributed memory 106).") in response to receiving updated recording data and dividing the updated recording data into updated clusters of information (Jain et al. ¶ [0042], "The transactions of the users are aggregated by the raw transaction aggregator 101 and stored as raw data in the first data repository 103. In another example herein, consider that a surveillance feed is connected to the system and data from the surveillance devices (such as video) are aggregated by the raw transaction aggregator 101 and stored in the first data repository 103." ¶ [0060], "The cluster management engine 108b can update clusters based on changes in the raw data/lower dimensionality data").
It would have been obvious to a person having ordinary skill in the art before the time of the effective filing date of the claimed invention of the instant application to modify Zhang et al. in view of Eun et al. in view of Paek et al. in view of Venkatraman et al. to incorporate Jain et al.’s cluster and tag updating because such a modification is the result of combining prior art elements according to known methods to yield predictable results. More specifically, Zhang et al.’s tagging method as modified by Jain et al.’s cluster and tag updating can yield a predictable result of improving cluster quality since clusters could be iteratively updated to stay relevant to current user data. Thus, a person of ordinary skill would have appreciated including in Zhang et al.’s tagging method the ability to do Jain et al.’s cluster and tag updating since the claimed invention is merely a combination of old elements, and in the combination each element merely would have performed the same function as it did separately, and one of ordinary skill in the art would have recognized that the results of the combination were predictable.
Reference Cited
The prior art made of record and not relied upon is considered pertinent to applicant’s disclosure.
US Patent 11518392 B1 to Sanchez discloses analyzing and tagging the workflow of a driving user, the workflow being related to distracted driving events and phone actions.
Conclusion
Applicant's amendment necessitated the new ground(s) of rejection presented in this Office action. Accordingly, THIS ACTION IS MADE FINAL. See MPEP § 706.07(a). Applicant is reminded of the extension of time policy as set forth in 37 CFR 1.136(a).
A shortened statutory period for reply to this final action is set to expire THREE MONTHS from the mailing date of this action. In the event a first reply is filed within TWO MONTHS of the mailing date of this final action and the advisory action is not mailed until after the end of the THREE-MONTH shortened statutory period, then the shortened statutory period will expire on the date the advisory action is mailed, and any 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.
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/JACOB B VOGT/ Examiner, Art Unit 2653
/Paras D Shah/ Supervisory Patent Examiner, Art Unit 2653
05/04/2026
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