DETAILED ACTION
Notice of Pre-AIA or AIA Status
The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA .
Response to Amendment
This communication is responsive to the applicant’s amendment dated 12/4/2025. The applicant amended claims 1, 3, 5, 8-9, 11, 13, 15, 17, 19, 23, and 27. Claims 2, 4, 7, 10, 12, 16, 18, 22, 24-26, and 28-30 have been cancelled. Lastly, claims 34-35 have been added as new claims.
Response to Arguments
Applicant's arguments with respect to 35 U.S.C. 101 (see Remarks pg. 10, line 1 – pg. 12, line 7) filed 12/4/2025 have been fully considered but they are not persuasive.
The applicant argues (pg. 11, line 10 – pg. 11, line 17) that generating and transmitting a “converted corresponding sentiment-neutral topic labels” realizes an improvement in “topic modeling by converting sentiment-oriented topic labels to sentiment- neutral topic labels in response to user queries for topic labels without sentiment". As a result, this preserve "useful information associated with the sentiment-oriented topic label while altering the sentiment-oriented topic label into a form satisfying the sentiment preference of the user query". The applicant cites the MPEP, the claims, and the specification to support his argument. The examiner respectfully disagrees. The examiner believes that what the applicant is claiming to be a technical improvement is not a technical improvement but part of the abstract idea (mental process/mathematical calculation). An inventive concept cannot be furnished by the abstract idea itself (MPEP 2106.05 (I)- An inventive concept "cannot be furnished by the unpatentable law of nature (or natural phenomenon or abstract idea) itself." Genetic Techs. Ltd. v. Merial LLC, 818 F.3d 1369, 1376, 118 USPQ2d 1541, 1546 (Fed. Cir. 2016). See also Alice Corp., 573 U.S. at 21-18, 110 USPQ2d at 1981 (citing Mayo, 566 U.S. at 78, 101 USPQ2d at 1968 (after determining that a claim is directed to a judicial exception, "we then ask, ‘[w]hat else is there in the claims before us?") (emphasis added)). Abstract is abstract whether it is new idea or a novel abstract idea of providing topic labels of a sentiment polarity from a user can be practically be done by a human in the mind. Additionally, the examiner views the transmitting limitation as extra-solution activity. The core process underlining that is simply providing data resulting from a calculation or determination of ranking of documents based off TF-IDF. That information is being sent over a network that is well known and routine conventional extra-solution activity that doesn’t constitute an inventive concept under 2A prong two and 2B. See MPEP 2106.05(d)(II)(i)- 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); but see DDR Holdings, LLC v. Hotels.com, L.P., 773 F.3d 1245, 1258, 113 USPQ2d 1097, 1106 (Fed. Cir. 2014) ("Unlike the claims in Ultramercial, the claims at issue here specify how interactions with the Internet are manipulated to yield a desired result‐‐a result that overrides the routine and conventional sequence of events ordinarily triggered by the click of a hyperlink." (emphasis added)). Therefore, the 35 U.S.C. 101 rejection is maintained.
Applicant's arguments with respect to 35 U.S.C. 103 (See Remarks, pg. 12, line 8 – pg. 15, line 22) filed 12/4/2025 have been fully considered but they are not persuasive.
First the applicant argues the cited references fail to teach or suggest TF-IDF for topic labels. In particular the applicant believes that Orbach’s interpretation of TF-IDF values are limited to words in documents rather than topic labelling. The examiner respectfully disagrees. Paragraph [0075] states, “the number of appearances of the specific word 210B in a corpus or a plurality 21 of documents 21A, pertaining to a specific tenant 20 or domain 20′”. The examiner interprets the words to be related to a specific domain or topic. In general, Orbach teaches a method and system for automatic topic detection in text. Therefore, the examiner believes Orbach discusses TF-IDF relating to topic labelling.
Next, the applicant argues that the cited references do not teach or suggest a “converted corresponding sentiment-neutral topic label”. The examiner respectfully disagrees. Sommer discusses in paragraph [0069] of monitoring changes of a metric related to sentiment. Those changes could be sentimentally neutral changes. The examiner interprets that could be from a metric (sentiment) that signifies a sentiment positive/negative metric. Additionally, the applicant argues that Sommer discusses a mechanism for classifying sentiment, however, the examiner views classifying sentiment similar to generating sentiment labels. Therefore, the examiner believes the Sommer teaches the recited limitation. In terms of the additional reference provided by examiner, the applicant believes Devarajan does not teach the limitation because Devarajan characterizes existing sentiment that changes over a period of time and that a sentiment value is not generated. The examiner respectfully disagrees. The examiner believes that changing sentiment can be a form of generating converted corresponding sentiment-neutral topic labels. In terms of the changing sentiment value, Devarajan teaches this in paragraph [0193], “the overall sentiment may not be a single sentiment value, but instead may include multiple sentiment values expressed over a period of time”. Therefore, the limitations are met by the prior art and the 35 U.S.C. 103 rejection is maintained.
Claim Rejections - 35 USC § 101
35 U.S.C. 101 reads as follows:
Whoever invents or discovers any new and useful process, machine, manufacture, or composition of matter, or any new and useful improvement thereof, may obtain a patent therefor, subject to the conditions and requirements of this title.
Claims 1, 3, 5-6, 8-9, 11, 13-15, 17, 19-21, 23, 27 and 31-35 are rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more.
Independent claims 1, 9, and 15 recites "generating a plurality of topic labels corresponding to a plurality of documents clustered into a plurality of topics, wherein the plurality of topic labels include a sentiment-oriented topic label and a sentiment-neutral topic label"; "generating a converted corresponding sentiment-neutral topic label corresponding to the sentiment-oriented topic label using a sentiment dictionary associating sentiment-neutral tokens and sentiment-oriented tokens for similar topics"; "calculating term frequency-inverse document frequency (TF-IDF) values for respective topic labels and corresponding pluralities of documents"; "receiving a neutral sentiment polarity from a user device"; "identifying a subset of the plurality of topic labels that satisfy the neutral sentiment polarity including the converted corresponding sentiment-neutral topic label"; and "transmitting the converted corresponding sentiment-neutral topic label of the subset of the plurality of topic labels to the user device, wherein the converted corresponding sentiment-neutral topic label has a higher TF-IDF value than other topic labels in the subset of the plurality of topic labels wherein the higher TF-IDF value is a term frequency (TF) multiplied by an inverse document frequency (IDF), wherein the TF is a first quotient of a number of times a token occurs in a topic divided by a sum of all tokens related to the topic, and wherein the IDF is a log of a second quotient of a total count of topics divided by a total count of topics containing the token".
These steps, as drafted, is a process that, under its broadest reasonable interpretation, covers performance of the limitation in the mind but for the recitation of generic computer components. That is, other than reciting “one or more computer readable storage media storing program instructions; and one or more processors which, in response to executing the program instructions” and “A computer program product comprising one or more computer readable storage media, and program instructions collectively stored on the one or more computer readable storage media, the program instructions comprising instructions configured to cause one or more processors”, nothing in the claim element precludes the steps from practically being achievable by a human using a pen and paper by categorizing documents by giving them topic labels based off their sentiment; changing the labels to a neutral labels; performing calculations based off the topic labels for the documents; receiving document sentiment feedback from a user; picking a topic label that matches with the sentiment; and returning that information to the user that shows the calculations with the highest correlation between the sentiment and the topic label. If a claim limitation, under its broadest reasonable interpretation, covers performance limitation in the mind but for the recitation of generic computer components, then it falls within the “Mental Process” grouping of abstract ideas. Accordingly, the claims recite an abstract idea.
This judicial exception is not integrated into a practical application because the claims recite the additional elements of “one or more computer readable storage media storing program instructions; and one or more processors which, in response to executing the program instructions” and “A computer program product comprising one or more computer readable storage media, and program instructions collectively stored on the one or more computer readable storage media, the program instructions comprising instructions configured to cause one or more processors”. These elements are recited at a high level of generality such that they would amount to no more than mere instructions to implement the abstract idea on conventional computer equipment. The claims does not point to a specific improvement in computers in their communication role or provide specific improvements in the way computers operate. The claims, as a whole, looking at the additional elements individually and in combination, does not integrate the abstract idea into a practical application. See MPEP 2106.04(d).
The claims do not include additional elements that are sufficient to amount to significantly more than the judicial exception because as discussed above with respect to integration of the abstract idea into a practical application, the additional elements of “one or more computer readable storage media storing program instructions; and one or more processors which, in response to executing the program instructions” and “A computer program product comprising one or more computer readable storage media, and program instructions collectively stored on the one or more computer readable storage media, the program instructions comprising instructions configured to cause one or more processors” to perform the claimed steps (“generating…”, “calculating…’, “receiving…”, identifying…”, “transmitting…”) amounts to no more than mere instructions to apply the exception using a generic computer component. Mere instructions to apply an exception using a generic computer component cannot provide an inventive concept. The claims are not patent eligible.
Dependent claims 3, 5-6, 8, 11, 13-14, 17, 19-20, 27, and 31-35 are also rejected for the same reasons provided in the rejection to the independent claims above. The dependent claims, including the further recited limitation, does not integrate the abstract idea into a practical application and the additional elements, taken individually and in combination do not contribute to an inventive concept. In other words, the dependent claims are directed to an abstract idea without significantly more.
Regarding independent claim 21, the claim recites “generating a sentiment-neutral topic label for a first plurality of documents clustered into a first topic”, “generating a sentiment-oriented topic label for a second plurality of documents clustered into a second topic”, “receiving a selected sentiment polarity from a user device, wherein the selected sentiment polarity is a neutral sentiment polarity”, “generating a converted corresponding sentiment-neutral topic label corresponding to the sentiment-oriented topic label using a sentiment dictionary associating sentiment-neutral tokens and sentiment-oriented tokens for similar topics”, and “and transmitting the sentiment-neutral topic label and the converted corresponding sentiment- neutral topic label to the user device, wherein the sentiment-neutral topic label and the converted corresponding sentiment-neutral topic label are ranked by term frequency-inverse document frequency (TF-IDF) values”.
These steps, as drafted, is a process that, under its broadest reasonable interpretation, covers performance of the limitation in the mind. Nothing in the claim element precludes the steps from practically being achievable by a human using a pen and paper by categorizing a document with a first sentiment neutral topic label; categorizing a document with a second sentiment-oriented topic label; receiving a document sentiment neutral feedback from a user; changing that feedback from a sentiment-neutral to sentiment-oriented topic label; and returning that information to the user that shows the calculations showing the highest correlation between sentiment and topic label. If a claim limitation, under its broadest reasonable interpretation, covers performance limitation in the mind, then it falls within the “Mental Process” grouping of abstract ideas. Accordingly, the claim recites an abstract idea.
This judicial exception is not integrated into a practical application because the claim does not recite any additional elements to perform the steps of the claim. The claim, as a whole, does not integrate the abstract idea into a practical application.
The claim does not include additional elements that are sufficient to amount to significantly more than the judicial exception because as discussed above with respect to integration of the abstract idea into a practical application, no additional elements are recited to perform the claimed steps (“generating…”, “generating…”, “receiving…”, “generating…”, “transmitting…”) and amounts to no more than mere instructions to apply the exception. Mere instructions to apply an exception cannot provide an inventive concept. The claim is not patent eligible.
Dependent claim 23 is also rejected for the same reasons provided in the rejection to the independent claims above. The dependent claims, including the further recited limitation, does not integrate the abstract idea into a practical application and the additional elements, taken individually and in combination do not contribute to an inventive concept. In other words, the dependent claim is directed to an abstract idea without significantly more.
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, 3, 5-6, 9, 11, 13-15, 17, and 19-21 are rejected under 35 U.S.C. 103 as being unpatentable over Sommer et al. US 20100262454 A1 (hereinafter Sommer) in view Orbach et al US 20220382982 A1 (hereinafter Orbach).
Regarding claims 1, 9, and 15, Sommer teaches generating a plurality of topic labels corresponding to a plurality of documents clustered into a plurality of topics, wherein the plurality of topic labels include a sentiment-oriented topic label and a sentiment-neutral topic label ([0055] “The process begins by assembling a group of d documents that are all related to or somehow germane to a particular topic or subject matter (step 102) From the onset, it should be appreciated that the intent of constructing the document sentiment vector space is to measure the sentimentality toward a topic or subject matter, so all documents in the group should be relevant to that topic, that is, have some relationship to the topic”, a unique sentiment binding label (topic label) is used to measure the sentiment of a group of documents based off the subject matter or topic ; [0058] “The sentimentality of a document toward a topic may be defined as being significant or insignificant”, the documents can be categorized as sentiment-oriented topics ; [0058] “Documents with neither positive nor negative sentiment have a neutral sentiment toward the topic and, therefore, have a "neutral" polarity toward the topic”, the documents can be categorized as sentiment-neutral topics );
generating a converted corresponding sentiment-neutral topic label corresponding to the sentiment-oriented topic label using a sentiment dictionary associating sentiment-neutral tokens and sentiment-oriented tokens for similar topics; the converted corresponding sentiment-neutral topic label; converted corresponding sentiment-neutral topic label; ([0059] “There may even be other instances where only documents with a neutral sentiment are to be considered, in those cases documents with a historical sentiment score of 0.4>sentiment_score>-0.4 will be sentiment-labeled”, the neutral sentiments are selected; [0069] ““The extrinsic metric to be monitored for change is then selected (step 204). The choice of metric to monitor should bear a direct relationship to sentiment of a reader and its value will change by some affirmative action undertaken by the reader that can be measured …for most metrics, movements or changes in its value should be bifurcated at least between sentimentally significant changes and sentimentally neutral changes; a threshold value between the two can be established. For others metrics, changing values may further reflect positive sentiment, neutral sentiment and negative sentiment and appropriate sentiment thresholds selected between each sentiment polarity”, given a certain threshold, sentiments can change from neutral sentiment to positive/negative sentiment topic label);
receiving a neutral sentiment polarity from a user device ([0009] “The sentiment polarity of any publication with respect to the topic can be accessed by semantically processing the publication into the document sentiment vector space. The publication's location in the document sentiment vector space is a measure of its sentiment polarity toward the topic”, selection of sentiment polarity used to identify topic labels);
identifying a subset of the plurality of topic labels that satisfy the neutral sentiment polarity including the converted corresponding sentiment-neutral topic label ([0057] “the sentiment toward the topic is assessed for each of the d documents in the group (step 104)”, the topic of each document is matched with the appropriate sentiment; [0059], [0069]);
Sommer fails to teach calculating term frequency-inverse document frequency (TF-IDF) values for respective topic labels and corresponding pluralities of documents; transmitting of the subset of the plurality of topic labels to the user device wherein the has a higher TF-IDF value than other topic labels in the subset of the plurality of topic labels, wherein the higher TF-IDF value is a term frequency (TF) multiplied by an inverse document frequency (IDF), wherein the TF is a first quotient of a number of times a token occurs in a topic divided by a sum of all tokens related to the topic, and wherein the IDF is a log of a second quotient of a total count of topics divided by a total count of topics containing the token;
However, Orbach teaches calculating term frequency-inverse document frequency (TF-IDF) values for respective topic labels and corresponding pluralities of documents ([0073] “a Term Frequency-Inverse Document Frequency (TF-IDF) score is a numerical statistic that may indicate how important a word is to a specific document in a collection or corpus of documents. TF-IDF score may for example be used as a weighting factor in automated searches of textual information retrieval and text mining. The TF-IDF score value may increases proportionally to the number of times a word appears in the specific document”, a TF-IDF calculation produces a score that indicates how important a word is in a document; [0031] “FIG. 3A is a block diagram, depicting a salience computation module, which may be included in a system for automatic topic detection in text, according to some embodiments of the invention”, 230 performs the TF-IDF calculations which is part of the module used to perform topic detection from a document (21A), [0075] “weight calculation module 230 may calculate a weight 230A of a specific word in relation to a specific document 21A as a TF-IDF function of (a) the number of appearances of the specific word 210B in the specific document 21A, and (b) the number of appearances of the specific word 210B in a corpus or a plurality 21 of documents 21A, pertaining to a specific tenant 20 or domain 20”);
transmitting of the subset of the plurality of topic labels to the user device wherein the has a higher TF-IDF value than other topic labels in the subset of the plurality of topic labels ([0075] “According to some embodiments, weight calculation module 230 may be a calculator of TF-IDF score, and weight 230A may be a TF-IDF score value”, the highest calculated TF-IDF score indicates the most relevant topic label ; [0062]“The output of such a process is a set of labels representing specific topics. It may be appreciated that the set of topic labels should be formed so as to maximize a variety (or minimal similarity) between labels, and that each label would be a topic that is of interest to the end user”, relevant topic labels based off TF-IDF scores are returned to the user).
wherein the higher TF-IDF value is a term frequency (TF) multiplied by an inverse document frequency (IDF), wherein the TF is a first quotient of a number of times a token occurs in a topic divided by a sum of all tokens related to the topic, wherein the IDF is a log of a second quotient of a total count of topics divided by a total count of topics containing the token (Eq. 1, [0077] “As shown in the example Eq. 1, it may be appreciated by a person skilled in the art that word weight 230A (e.g., α[w.sub.i]) may be calculated, for example, as an Inverse Document Frequency function (e.g., IDF) of the TF-IDF function”; [0075])
Sommer in view of Orbach are considered to be analogous to the claimed invention because both are in the same field of topic detection. Therefore, it would have been obvious to someone of ordinary skill in the art before the effective filing date of the claimed invention to have modified the method of ranking document sentimentality based on an extrinsic measure of sentiment to a topic of Sommer with the Term Frequency-Inverse Document Frequency score calculation taught by Orbach in order to improve automatic assessment and filtering of topic labels from a text document (see Orbach [0007]).
While Orbach does not specifically teach “a number of times a token occurs in a topic divided by a sum of all tokens related to the topic” or “a log of a second quotient of a total count of topics divided by a total count of topics containing the token”, it would have been obvious to someone of ordinary skill in the art before the effective filing date of the claimed invention to substitute the variables of the TF-IDF equation with different variables related to topic labelling. This would have yielded predictable results and provided an alternative method of calculating TF-IDF specifically for topic labelling.
Regarding claims 3, 11, and 17, Sommer in view of Orbach teaches all the limitations in claims 1, 9, and 15 upon which claims 3, 11, and 17 depend.
Sommer additionally teaches wherein the subset of the plurality of topic labels includes both the sentiment-neutral topic label and the converted corresponding sentiment-neutral topic label ([0069] “For most metrics, movements or changes in its value should be bifurcated at least between sentimentally significant changes and sentimentally neutral changes; a threshold value between the two can be established. For others metrics, changing values may further reflect positive sentiment, neutral sentiment and negative sentiment and appropriate sentiment thresholds selected between each sentiment polarity”, a group of topic labels can include neutral topic labels and topic labels that have changed by crossing over a threshold value).
Regarding claims 5, 13, and 19, Sommer in view of Orbach teaches all the limitations in claims 1, 9, and 15 upon which claims 5, 13, and 19 depend.
Sommer additionally teaches wherein the method further comprises receiving a non-neutral sentiment polarity from the user device (FIG. 8, 802, [0090] “the query may contain a user tag or a keyword label associated with the topic”); and
removing topic labels with sentiments that do not match the non-neutral sentiment polarity from the plurality of topic labels ([0067] “Clearly, some constraints should be implemented in order to ensure that changes in the value of the extrinsic metric are directly related to the sentiment of the reader toward the topic from reading a related article or publication. For instance, care should be taken in the selection of a proper extrinsic metric for measuring sentiment, action thresholds should be established for changes in the value of the metric that are indicative of significant sentiment and a timeframe for monitoring the extrinsic metric should be identified where changes in the value of the metric would reasonably infer the sentimentality from reading the publication”, when action thresholds are reached, this causes for a change sentiment topic label based off the metric being used).
Regarding claims 6, 14, and 20, Sommer in view of Orbach teaches all the limitations in claims 5, 13, and 19 upon which claims 6, 14, and 20 depend.
Sommer additionally teaches except wherein the method further comprises: tagging respective documents with respective sentiment tags ([0129] “User tags and keyword labels attached to a document provide users with a treasure trove of synonymic information concerning a publication. For instance, associated with the set of sentiment-ranked documents returned from a query are tags that summarize and or describe the sentimentality of each of the documents”, tags are placed on document that describes its sentiment); and removing documents from the plurality of documents with sentiment tags that do not match the non-neutral sentiment polarity ([0131] “Next, the relatedness of similar terms, words and keyword labels to the user tag is determined using, for instance statistical and probabilistic similarities, latent semantic models, etc. (step 1810) and additional action publications are returned to the user containing the more related similar terms tags and keyword labels (step 1812). Many of these action publications will not contain the original user tag, but are all related to the document meanings of the original set of action documents returned to the user in step 1804”, how related the tag is to the sentiment topic label is determined).
Regarding claim 21, Sommer teaches generating a sentiment-neutral topic label for a first plurality of documents clustered into a first topic; generating a sentiment-oriented topic label for a second plurality of documents clustered into a second topic ([0055] “The process begins by assembling a group of d documents that are all related to or somehow germane to a particular topic or subject matter (step 102) From the onset, it should be appreciated that the intent of constructing the document sentiment vector space is to measure the sentimentality toward a topic or subject matter, so all documents in the group should be relevant to that topic, that is, have some relationship to the topic”, a unique sentiment binding label (topic label) is used to measure the sentiment of a group of documents based off the subject matter or topic ; [0058] “The sentimentality of a document toward a topic may be defined as being significant or insignificant”, the documents can be categorized as sentiment-oriented topics ; [0058] “Documents with neither positive nor negative sentiment have a neutral sentiment toward the topic and, therefore, have a "neutral" polarity toward the topic”, the documents can be categorized as sentiment-neutral topics.);
receiving a selected sentiment polarity from a user device, wherein the selected sentiment polarity is a neutral sentiment polarity ([0054] FIG 1 “the construction of the document sentiment vector space allows for two basic types of sentiment analysis: mining sentiment information directly from the sentiment document vector space (i.e., using query strings and the like, and/or analyzing patterns of term occurrence and occurrence frequencies using column document and row term vectors and/or the term dictionaries”, a neutral query (sentiment polarity) is received through a sentiment document vector space);
generating a converted corresponding sentiment-neutral topic label corresponding to the sentiment-oriented topic label ([0059] “There may even be other instances where only documents with a neutral sentiment are to be considered, in those cases documents with a historical sentiment score of 0.4>sentiment_score>-0.4 will be sentiment-labeled”, the neutral sentiments are selected; [0069] “The extrinsic metric to be monitored for change is then selected…for most metrics, movements or changes in its value should be bifurcated at least between sentimentally significant changes and sentimentally neutral changes; a threshold value between the two can be established. For others metrics, changing values may further reflect positive sentiment, neutral sentiment and negative sentiment and appropriate sentiment thresholds selected between each sentiment polarity”, given a certain threshold, sentiments can change from neutral sentiment to positive/negative sentiment topic label);
using a sentiment dictionary associating sentiment-neutral tokens and sentiment-oriented tokens for similar topics (FIG. 4, 410, [0088] “The dictionaries and document vectors for the contemporaneous publications can then be analyzed for terms occurring in the action publications, having a high frequency of occurrences, or co-occurring in many action publications, that may be identified as having sentimental meaning toward the topic”);
Sommer fails to teach transmitting the sentiment-neutral topic label and the converted corresponding sentiment- neutral topic label to the user device, wherein the sentiment-neutral topic label and the converted corresponding sentiment-neutral topic label are ranked by term frequency-inverse document frequency (TF-IDF) values.
However, Orbach teaches transmitting the sentiment-neutral topic label and the converted corresponding sentiment- neutral topic label to the user device, wherein the sentiment-neutral topic label and the converted corresponding sentiment-neutral topic label are ranked by term frequency-inverse document frequency (TF-IDF) values ([0075] “According to some embodiments, weight calculation module 230 may be a calculator of TF-IDF score, and weight 230A may be a TF-IDF score value”, the highest calculated TF-IDF score indicates the most relevant topic label ; [0062]“The output of such a process is a set of labels representing specific topics. It may be appreciated that the set of topic labels should be formed so as to maximize a variety (or minimal similarity) between labels, and that each label would be a topic that is of interest to the end user”, ranked relevant topic labels based off TF-IDF scores are returned to the user).
Sommer in view of Orbach are considered to be analogous to the claimed invention because both are in the same field of topic detection. Therefore, it would have been obvious to someone of ordinary skill in the art before the effective filing date of the claimed invention to have modified the method of ranking document sentimentality based on an extrinsic measure of sentiment to a topic of Sommer with the Term Frequency-Inverse Document Frequency score calculation taught by Orbach in order to improve automatic assessment and filtering of topic labels from a text document (see Orbach [0007]).
Claims 8 and 23 are rejected under 35 U.S.C. 103 as being unpatentable over Sommer in view of Orbach, as shown above in claim 1, in further view of Hoefelmeyer et al US 20090076793 A1 (hereinafter Hoefelmeyer).
Regarding claim 8 Sommer in view of Orbach teaches all the limitations in claims 3, upon which claim 8 depends.
Additionally, Sommer teaches based on a number of converted corresponding sentiment-neutral topic labels generated by the software ([0059]; [0069])
Sommer in view of Orbach fails to teach wherein the method is performed by one or more computers according to software that is downloaded to the one or more computers from a remote data processing system wherein the method further comprises: metering a usage of the software; and generating an invoice based on metering the usage
Additionally, Hoefelmeyer teaches wherein the method is performed by one or more computers according to software that is downloaded to the one or more computers from a remote data processing system ([0041] “the instructions for carrying out at least part of the various exemplary embodiments may initially be borne on a magnetic disk of a remote computer. In such a scenario, the remote computer loads the instructions into main memory and sends the instructions over a telephone line using a modem. A modem of a local computer system receives the data on the telephone line and uses an infrared transmitter to convert the data to an infrared signal and transmit the infrared signal to a portable computing device, such as a personal digital assistant (PDA) or a laptop”).
wherein the method further comprises: metering a usage of the software; and generating an invoice based on metering the usage ([0015] “As part of the managed service, a service provider maintains the translation service platform 101 and employs a billing system 115 to invoice its subscribers. The billing system 115 operates in conjunction with the translation applications 103 to accurately track usage of the service and to generate invoices based on the usage”).
Sommer in view of Orbach in view of Hoefelmeyer are considered to be analogous to the claimed invention because both are in the same field of electric digital data processing. Therefore, it would have been obvious to someone of ordinary skill in the art before the effective filing date of the claimed invention to have modified the topic classification of Sommer in view of Orbach with the technique of receiving instructions from a remote computer taught by Hoefelmeyer in order to improve a means for providing real-time language translation (see Hoefelmeyer [0010]).
Regarding claim 23, Sommer in view of Orbach teaches all the limitations of claims 21, upon which claim 23 depends.
Sommer in view of Orbach fails to teach wherein the method is performed by one or more computers according to software that is downloaded to the one or more computers from a remote data processing system: wherein the method further comprises: metering a usage of the software based on a number of endpoints using the software; and generating an invoice based on metering the usage
However, Hoefelmeyer teaches wherein the method is performed by one or more computers according to software that is downloaded to the one or more computers from a remote data processing system: wherein the method further comprises: metering a usage of the software based on a number of endpoints using the software; and generating an invoice based on metering the usage
([0041] “the instructions for carrying out at least part of the various exemplary embodiments may initially be borne on a magnetic disk of a remote computer. In such a scenario, the remote computer loads the instructions into main memory and sends the instructions over a telephone line using a modem. A modem of a local computer system receives the data on the telephone line and uses an infrared transmitter to convert the data to an infrared signal and transmit the infrared signal to a portable computing device, such as a personal digital assistant (PDA) or a laptop”; [0015] “As part of the managed service, a service provider maintains the translation service platform 101 and employs a billing system 115 to invoice its subscribers. The billing system 115 operates in conjunction with the translation applications 103 to accurately track usage of the service and to generate invoices based on the usage”; [0031], FIG. 5, 501, examiner interprets durations as endpoints).
Sommer in view of Orbach in view of Hoefelmeyer are considered to be analogous to the claimed invention because both are in the same field of electric digital data processing. Therefore, it would have been obvious to someone of ordinary skill in the art before the effective filing date of the claimed invention to have modified the topic classification of Sommer in view of Orbach with the technique of receiving instructions from a remote computer taught by Hoefelmeyer in order to improve a means for providing real-time language translation (see Hoefelmeyer [0010]).
Claims 27 and 34-35 is rejected under 35 U.S.C. 103 as being unpatentable over Sommer in view of Orbach, as shown above in claim 1, in further view of Novak et al. US 11341337 B1 (hereinafter Novak).
Regarding claim 27, Sommer in view of Orbach teaches all the limitations of claims 1, upon which claim 27 depends.
Sommer in view of Orbach fails to teach wherein the generating is performed using a trained machine learning model, and wherein the trained machine learning model is a Non- Negative Matrix Factorization (NMF) model.
However, Novak teaches wherein the generating is performed using a trained machine learning model, and wherein the trained machine learning model is a Non- Negative Matrix Factorization (NMF) model ([Column 11, line 45-50] “the topic analysis engine 4 applies use one of the existing techniques or their variants to generate a list of topics 50, including: Latent Semantic Analysis (LSA), Latent Dirichlet Allocation (LDA), Non-Negative Matrix Factorization (NMF), Parallel Latent Dirichlet Allocation (PLDA), Pachinko Allocation Model (PAM) etc ”)
Sommer in view of Orbach in view of Novak are considered to be analogous to the claimed invention because all are in the same field of sentiment analysis. Therefore, it would have been obvious to someone of ordinary skill in the art before the effective filing date of the claimed invention to have modified the topic classification of Sommer in view of Orbach with the technique of using Non- Negative Matrix Factorization (NMF) taught by Novak in order to improve messaging information management and summarization, using a combined user-post tag-based categorization system, rule-based filtering and natural language processing (see Novak [Column 1, line 8-11]).
Regarding claim 34, Sommer in view of Orbach teaches all the limitations of claims 1, upon which claim 34 depends.
Sommer in view of Orbach fails to teach wherein the generating is performed using a trained machine learning model, and wherein the trained machine learning model is a Parallel Latent Dirichlet Allocation (PLDA) model
However, Novak teaches wherein the generating is performed using a trained machine learning model, and wherein the trained machine learning model is a Parallel Latent Dirichlet Allocation (PLDA) model ([Column 11, line 45-50] “the topic analysis engine 4 applies use one of the existing techniques or their variants to generate a list of topics 50, including: Latent Semantic Analysis (LSA), Latent Dirichlet Allocation (LDA), Non-Negative Matrix Factorization (NMF), Parallel Latent Dirichlet Allocation (PLDA), Pachinko Allocation Model (PAM) etc ”)
Sommer in view of Orbach in view of Novak are considered to be analogous to the claimed invention because all are in the same field of sentiment analysis. Therefore, it would have been obvious to someone of ordinary skill in the art before the effective filing date of the claimed invention to have modified the topic classification of Sommer in view of Orbach with the technique of using Non- Negative Matrix Factorization (NMF) taught by Novak in order to improve messaging information management and summarization, using a combined user-post tag-based categorization system, rule-based filtering and natural language processing (see Novak [Column 1, line 8-11]).
Regarding claim 35, Sommer in view of Orbach teaches all the limitations of claims 1, upon which claim 35 depends.
Sommer in view of Orbach fails to teach wherein the generating is performed using a trained machine learning model, and wherein the trained machine learning model is a Pachinko Allocation Model (PAM)
However, Novak teaches wherein the generating is performed using a trained machine learning model, and wherein the trained machine learning model is a Pachinko Allocation Model (PAM) ([Column 11, line 45-50] “the topic analysis engine 4 applies use one of the existing techniques or their variants to generate a list of topics 50, including: Latent Semantic Analysis (LSA), Latent Dirichlet Allocation (LDA), Non-Negative Matrix Factorization (NMF), Parallel Latent Dirichlet Allocation (PLDA), Pachinko Allocation Model (PAM) etc ”)
Sommer in view of Orbach in view of Novak are considered to be analogous to the claimed invention because all are in the same field of sentiment analysis. Therefore, it would have been obvious to someone of ordinary skill in the art before the effective filing date of the claimed invention to have modified the topic classification of Sommer in view of Orbach with the technique of using Non- Negative Matrix Factorization (NMF) taught by Novak in order to improve messaging information management and summarization, using a combined user-post tag-based categorization system, rule-based filtering and natural language processing (see Novak [Column 1, line 8-11]).
Claims 31-33 are rejected under 35 U.S.C. 103 as being unpatentable over Sommer in view of Orbach, as shown above in claim 1, in further view of Kannu et al. US 10754883 B1 (hereinafter Kannu).
Regarding claim 31, Sommer in view of Orbach teaches all of the limitations of claim 1, upon which claim 31 depends.
Sommer in view of Orbach fails to teach wherein the generating the plurality of topic labels is performed using word embeddings
However, Kannu teaches wherein the generating the plurality of topic labels is performed using word embeddings ([Column 6, line 40-43] “In one embodiment of the present invention, a supervised learning approach uses a set of features (an n-dimensional “feature vector”) that are chosen for their effectiveness in separating desired and undesired entries”, examiner interprets feature vector as word embeddings)
Sommer in view of Orbach in view of Kannu are considered to be analogous to the claimed invention because both are in the same field of electric digital data processing. Therefore, it would have been obvious to someone of ordinary skill in the art before the effective filing date of the claimed invention to have modified the topic classification of Sommer in view of Orbach with the technique of using word embeddings when generating topic labels taught by Kannu in order to improve automated generation of insights from online or offline content data (see Kannu [Column 1, line 14-15]).
Regarding claim 32, Sommer in view of Orbach teaches all of the limitations of claim 1, upon which claim 32 depends.
Sommer in view of Orbach fails to teach wherein the generating the plurality of topic labels is performed using word2vec.
However, Kannu teaches wherein the generating the plurality of topic labels is performed using word2vec ([Column 6, line 58-61] “Examples of algorithms and corresponding classifiers used in supervised and unsupervised methods include, but not limited to, LDA2Vec, neural attention method, topic modelling, joint sentiment topic model, and Word2Vec”)
Sommer in view of Orbach in view of Kannu are considered to be analogous to the claimed invention because both are in the same field of electric digital data processing. Therefore, it would have been obvious to someone of ordinary skill in the art before the effective filing date of the claimed invention to have modified the topic classification of Sommer in view of Orbach with the technique of using word2vec when generating topic labels taught by Kannu in order to improve automated generation of insights from online or offline content data (see Kannu [Column 1, line 14-15]).
Regarding claim 33, Sommer in view of Orbach teaches all of the limitations of claim 1, upon which claim 33 depends.
Sommer in view of Orbach fails to teach wherein the generating the plurality of topic labels is performed using lda2vec.
However, Kannu teaches wherein the generating the plurality of topic labels is performed using lda2vec ([Column 6, line 58-61] “Examples of algorithms and corresponding classifiers used in supervised and unsupervised methods include, but not limited to, LDA2Vec, neural attention method, topic modelling, joint sentiment topic model, and Word2Vec”)
Sommer in view of Orbach in view of Kannu are considered to be analogous to the claimed invention because both are in the same field of electric digital data processing. Therefore, it would have been obvious to someone of ordinary skill in the art before the effective filing date of the claimed invention to have modified the topic classification of Sommer in view of Orbach with the technique of using LDA2Vec when generating topic labels taught by Kannu in order to improve automated generation of insights from online or offline content data (see Kannu [Column 1, line 14-15]).
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure.
Wang et al. (US 20150286718 A1) teaches a method of identifying topics in lecture videos may include receiving lecture video metadata, learning courses metadata, and a lecture video transcript (transcript). The transcript may include transcribed text of a lecture video (video). The method may include discovering candidate learning courses related to the video based on a measured similarity between the video metadata and the learning courses metadata. The method may include extracting key phrases from learning materials of the candidate learning courses. The method may also include assigning weights to the extracted key phrases based on a position of the extracted key phrases and a frequency with which the extracted key phrases appear, and the discovered candidate learning course in which the key phrases appear. The method may include apportioning the video into topic-specific portions based on topic segments generated in the transcript, the presence of the extracted key phrases therein, and the assigned weights.
Abudalfa et al. (US 20200349229 A1) teaches methods for classification of microblogs using semi-supervised open domain targeted sentiment classification. A hidden Markov model support vector machine (SVM HMM) is trained with a training dataset combined with discrete features. A portion of the training dataset is clustered by k-means clustering to generate cluster IDs which are normalized and combined with the discrete features. After formatting, the combined dataset is applied to the SVM HMM and the C parameter, which is optimized by calculating a zero-one error at each iteration. The open domain targeted sentiment classification methods uses less labelled data than previous sentiment analysis techniques, thus decreasing processing costs. Additionally, a supervised learning model for improving the accuracy of open domain targeted sentiment classification is presented using an SVM HMM.
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.
Any inquiry concerning this communication or earlier communications from the examiner should be directed to ZEESHAN SHAIKH whose telephone number is (703)756-1730. The examiner can normally be reached Monday-Friday 7:30AM-5:00PM.
Examiner interviews are available via telephone, in-person, and video conferencing using a USPTO supplied web-based collaboration tool. To schedule an interview, applicant is encouraged to use the USPTO Automated Interview Request (AIR) at http://www.uspto.gov/interviewpractice.
If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, Richemond Dorvil can be reached at (571) 272-7602. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
Information regarding the status of published or unpublished applications may be obtained from Patent Center. Unpublished application information in Patent Center is available to registered users. To file and manage patent submissions in Patent Center, visit: https://patentcenter.uspto.gov. Visit https://www.uspto.gov/patents/apply/patent-center for more information about Patent Center and https://www.uspto.gov/patents/docx for information about filing in DOCX format. For additional questions, contact the Electronic Business Center (EBC) at 866-217-9197 (toll-free). If you would like assistance from a USPTO Customer Service Representative, call 800-786-9199 (IN USA OR CANADA) or 571-272-1000.
/ZEESHAN MAHMOOD SHAIKH/Examiner, Art Unit 2658
/RICHEMOND DORVIL/Supervisory Patent Examiner, Art Unit 2658
Prosecution Insights