DETAILED ACTION
This Office Action is in response to the request for continued examination entered on 04/15/2026. Claims 2-4, 6-7, 9-14, 16-20 are amended. Claims 2-21 are pending.
Continued Examination Under 37 CFR 1.114
A request for continued examination under 37 CFR 1.114, including the fee set forth in 37 CFR 1.17(e), was filed in this application after final rejection. Since this application is eligible for continued examination under 37 CFR 1.114, and the fee set forth in 37 CFR 1.17(e) has been timely paid, the finality of the previous Office action has been withdrawn pursuant to 37 CFR 1.114. Applicant's submission filed on 04/15/2026 has been entered.
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 .
Double Patenting
The nonstatutory double patenting rejection is based on a judicially created doctrine grounded in public policy (a policy reflected in the statute) so as to prevent the unjustified or improper timewise extension of the “right to exclude” granted by a patent and to prevent possible harassment by multiple assignees. A nonstatutory double patenting rejection is appropriate where the conflicting claims are not identical, but at least one examined application claim is not patentably distinct from the reference claim(s) because the examined application claim is either anticipated by, or would have been obvious over, the reference claim(s). See, e.g., In re Berg, 140 F.3d 1428, 46 USPQ2d 1226 (Fed. Cir. 1998); In re Goodman, 11 F.3d 1046, 29 USPQ2d 2010 (Fed. Cir. 1993); In re Longi, 759 F.2d 887, 225 USPQ 645 (Fed. Cir. 1985); In re Van Ornum, 686 F.2d 937, 214 USPQ 761 (CCPA 1982); In re Vogel, 422 F.2d 438, 164 USPQ 619 (CCPA 1970); In re Thorington, 418 F.2d 528, 163 USPQ 644 (CCPA 1969).
A timely filed terminal disclaimer in compliance with 37 CFR 1.321(c) or 1.321(d) may be used to overcome an actual or provisional rejection based on nonstatutory double patenting provided the reference application or patent either is shown to be commonly owned with the examined application, or claims an invention made as a result of activities undertaken within the scope of a joint research agreement. See MPEP § 717.02 for applications subject to examination under the first inventor to file provisions of the AIA as explained in MPEP § 2159. See MPEP § 2146 et seq. for applications not subject to examination under the first inventor to file provisions of the AIA . A terminal disclaimer must be signed in compliance with 37 CFR 1.321(b).
The filing of a terminal disclaimer by itself is not a complete reply to a nonstatutory double patenting (NSDP) rejection. A complete reply requires that the terminal disclaimer be accompanied by a reply requesting reconsideration of the prior Office action. Even where the NSDP rejection is provisional the reply must be complete. See MPEP § 804, subsection I.B.1. For a reply to a non-final Office action, see 37 CFR 1.111(a). For a reply to final Office action, see 37 CFR 1.113(c). A request for reconsideration while not provided for in 37 CFR 1.113(c) may be filed after final for consideration. See MPEP §§ 706.07(e) and 714.13.
The USPTO Internet website contains terminal disclaimer forms which may be used. Please visit www.uspto.gov/patent/patents-forms. The actual filing date of the application in which the form is filed determines what form (e.g., PTO/SB/25, PTO/SB/26, PTO/AIA /25, or PTO/AIA /26) should be used. A web-based eTerminal Disclaimer may be filled out completely online using web-screens. An eTerminal Disclaimer that meets all requirements is auto-processed and approved immediately upon submission. For more information about eTerminal Disclaimers, refer to www.uspto.gov/patents/apply/applying-online/eterminal-disclaimer.
Claims 2-21 are rejected on the ground of nonstatutory double patenting as being unpatentable over claims 1-20 of U.S. Patent No. 11,403,557 in view of Deurloo (US PG Pub. 2013/0275527). Although the claims at issue are not identical, they are not patentably distinct from each other because the instant application claims are a broader version of the claims that appear in the Patent, as they are both directed to the use of a model to identify topics in the corpus of documents and computes how often each topic contributes to the topic in order to generate a map to display the contributions between the topics and the words in a hierarchical manner. Furthermore, the claim as amended recites the limitations “comparing the first map to a stored second map to identify a newly trending topic, wherein the stored second map is generated based on applying a second model to a second corpus of documents wherein the second model is trained such that the second model reflects baseline topics in the second corpus, wherein the second model is applied to the second corpus of documents before the first model is applied to the first corpus; and launching an early warning message system to issue a warning message indicating the identified newly trending topic”, which are not taught by the Patent, however, the combination of Prior art Deurloo and Mo teaches the limitations, as it can be seen for example at Deurloo’s paragraph [0048-0051] and Mo’s p. 3 (see rejection below for more detailed explanation). It would have been obvious to one of ordinary skill in the art before the effective filing date to modify the teachings of Sweeney with the above teachings of Deurloo and Mo in order to provide an early warning using a visual advantage for seeing trending topics and compare performance of models to provide more informative and less ambiguous results to viewers.
Claim Rejections - 35 USC § 112
The following is a quotation of the first paragraph of 35 U.S.C. 112(a):
(a) IN GENERAL.—The specification shall contain a written description of the invention, and of the manner and process of making and using it, in such full, clear, concise, and exact terms as to enable any person skilled in the art to which it pertains, or with which it is most nearly connected, to make and use the same, and shall set forth the best mode contemplated by the inventor or joint inventor of carrying out the invention.
The following is a quotation of the first paragraph of pre-AIA 35 U.S.C. 112:
The specification shall contain a written description of the invention, and of the manner and process of making and using it, in such full, clear, concise, and exact terms as to enable any person skilled in the art to which it pertains, or with which it is most nearly connected, to make and use the same, and shall set forth the best mode contemplated by the inventor of carrying out his invention.
Claims 2-21 are rejected under 35 U.S.C. 112(a) or 35 U.S.C. 112 (pre-AIA ), first paragraph, as failing to comply with the written description requirement. The claim(s) contains subject matter which was not described in the specification in such a way as to reasonably convey to one skilled in the relevant art that the inventor or a joint inventor, or for pre-AIA the inventor(s), at the time the application was filed, had possession of the claimed invention.
Independent claim 2 (and analogous claims 9 and 16) recite the limitation (as amended) “wherein the stored second map is generated based on applying a second model to a second corpus of documents wherein the second model is trained such that the second model reflects baseline topics in the second corpus, wherein the second model is applied to the second corpus of documents before the first model is applied to the first corpus”. Said limitation is considered to incorporate new matter in the claim which does not contain support in the original disclosure. Specification at [0059] recites: “In some embodiments, users may retain point-in-time versions of models and may also retain other versions of the model that are more frequently refined based upon corpus received from current events. The point-in-time model may be considered a 'Champion' model representing a baseline topic behavior profile for the enterprise at that given point in time. The more frequently updated models reflect a more current point in time topic profile for an enterprise and may be considered a 'Challenger' model. According to one aspect, as will be described in more detail below, the visualizer may be used to visually compare the challenger model corpus result to the champion model corpus result to expose new topic trends within the enterprise. In one embodiment, the history button, when selected, displays to the user a training history for the model associated with the model identifier 50”. This paragraph describes the idea of two models, as it appears there is a baseline model being the called “champion” model, and an updated model being called the “challenger” model, however, it seems that these two models are applied to the same corpus in order to determine new topics trends on the same corpus, based on the use of the “challenger” model, as being updated. Therefore, examiner cannot find support in the Specification for this newly added limitation, and cannot conclude how exactly there could be two different corpus, and two different models applied to each corpus separately, and finally conclude a trend. Claims 3-8, 10-15, 17-21 are rejected as being dependent claims. Appropriate correction is required.
Claim Rejections - 35 USC § 112
The following is a quotation of 35 U.S.C. 112(b):
(b) CONCLUSION.—The specification shall conclude with one or more claims particularly pointing out and distinctly claiming the subject matter which the inventor or a joint inventor regards as the invention.
The following is a quotation of 35 U.S.C. 112 (pre-AIA ), second paragraph:
The specification shall conclude with one or more claims particularly pointing out and distinctly claiming the subject matter which the applicant regards as his invention.
Claims 2-21 are rejected under 35 U.S.C. 112(b) or 35 U.S.C. 112 (pre-AIA ), second paragraph, as being indefinite for failing to particularly point out and distinctly claim the subject matter which the inventor or a joint inventor (or for applications subject to pre-AIA 35 U.S.C. 112, the applicant), regards as the invention.
Independent claim 2 (and analogous claims 9 and 16) recites the limitation (as amended) “wherein the stored second map is generated based on applying a second model to a second corpus of documents wherein the second model is trained such that the second model reflects baseline topics in the second corpus, wherein the second model is applied to the second corpus of documents before the first model is applied to the first corpus”, and this limitation is considered unclear and indefinite. The limitation recites a first model applied to a first corpus, and a second model applied to a second corpus, however, this second model is being declared at the end of the claim (as amended) but it is executed prior to the first model (which is being described at the first limitation/ beginning of the claim), therefore, the order of execution of the limitations is unclear. It would be reasonable that the first model is executed first, and the second model executed afterwards. It is further unclear how the second corpus and the first corpus are connected or related so that a trending can be identified in the first corpus using the first model. For purposes of examination, Examiner will interpret the two models, one as a baseline model and another one as a different model applied to determine newly trends. Clarification is required.
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 2-21 stand rejected under 35 U.S.C. 101 because the claimed invention is directed to a judicial exception without significantly more.
Step 1 analysis:
In the instant case, the claims are directed to a method, non-transitory computer-readable medium, and a system. Thus, each of the claims falls within one of the four statutory categories (i.e., process, machine, manufacture, or composition of matter).
Step 2A analysis:
Based on the claims being determined to be within of the four categories (Step 1), it must be determined if the claims are directed to a judicial exception (i.e., law of nature, natural phenomenon, and abstract idea), in this case the claims fall within the judicial exception of an abstract idea. Specifically the abstract idea of Mental Processes- “Concepts performed in the human mind (including an observation, evaluation, judgment, opinion)”.
Step 2A: Prong 1 analysis:
The claim(s) recite(s):
Claim 2:
“determining a first corpus of documents and a first model to apply to the first corpus” - this limitation recites determining a model to apply to a certain corps of documents, which are observation, evaluation and judgment steps, being mental processes;
“iteratively applying the first model to the first corpus to identify topics in the first corpus of documents, each topic associated with one or more words” - this limitation recites identifying topics in the corpus of documents, which are observation, evaluation and judgment steps, being mental processes. The use of the model is discussed next at Step2A: Prong 2;
“determining, for each topic, contribution values for the one or more words to the topic, wherein each contribution value corresponds to one of the one or more words, and each contribution value indicates a frequency one of the one or more words contributes to the topic” - this limitation recites determining to a topic indicating a frequency, which are observation, evaluation and judgment steps, being mental processes;
“generating a first map comprising the one or more words, the contribution values, and the topics, wherein each word of the one or more words is mapped to a particular topic and has a corresponding contribution value assigned thereto in the first map” - this limitation recites mapping words with topics according to their contribution values, which are observation, evaluation and judgment steps, being mental processes; and
“comparing the first map to a stored second map to identify a newly trending topic” - this limitation recites a comparison made to identify a new trending topic, which are observation of the maps, evaluation and judgment steps, being mental processes.
Step 2A: Prong 2 analysis:
This judicial exception is not integrated into a practical application because it only recites these additional elements:
using a model – using a model to identify topics (which is the recited abstract idea at Prong 1 above) in the corpus of documents is recited at a high level of generality, therefore, it amounts to mere instructions to apply a judicial exception on a computer (see MPEP 2106.05(f));
“storing, in storage, the first map” – a step of storing data of the result of the abstract idea does not add a meaningful limitation to the process of mapping words with the topics according to their contribution values, therefore, this is considered an insignificant extra solution activity per MPEP 2106.05 (g);
“wherein the stored second map is generated based on applying a second model to a second corpus of documents wherein the second model is trained such that the second model reflects baseline topics in the second corpus, wherein the second model is applied to the second corpus of documents before the first model is applied to the first corpus” - using two models, one as a baseline and one as an additional one, for the purpose of identifying topics (which is the recited abstract idea at Prong 1 above) is recited at a high level of generality, therefore, it amounts to mere instructions to apply a judicial exception on a computer (see MPEP 2106.05(f));
“launching an early warning message system to issue a warning message indicating the identified newly trending topic”- a final step of launching or displaying a message of the result of the trending topic is one of the examples that the courts have described as merely indicating a field of use or technological environment in which to apply a judicial exception (see 2106.05(h) (vi)- displaying the results of collection and analysis of data.
Accordingly, these additional elements do not integrate the abstract idea into a practical application because it does not impose any meaningful limits on practicing the abstract idea. The claims are directed to an abstract idea.
Step 2B analysis:
The claims do not include additional elements that are sufficient to amount to significantly more than the judicial exception. As discussed above with respect to integration of the abstract idea into a practical application, the additional elements explained above amount to mere instructions to apply an exception, insignificant extra solution activities and merely indicating a field of use or technological environment in which to apply a judicial exception.
Moreover, re-evaluation of any additional elements or combination of elements that were considered to be insignificant extra-solution activity are needed to determine if they are considered well-understood, routine and conventional limitations:
“storing, in storage, the first map” – This limitation is directed to storing or retrieving information in memory, which has been recognized by the courts (as per Versata Dev. Group, Inc. v. SAP Am., Inc., 793 F.3d 1306, 1334, 115 USPQ2d 1681, 1701 (Fed. Cir. 2015)) as well-understood, routine, conventional activity (See MPEP 2106.05(d)(iv)).
The claims are not patent eligible.
Independent claims 9 and 16 are analogous claims, therefore the same rejection and rationale applies to them.
In addition, Claim 9 recites the additional elements analyzed under Step 2A: prong 2 and Step 2B:
Claim 9: “the computer- readable storage medium including instructions that when executed by a processor, cause the processor to”- this medium and processors are recited at a high level of generality, and it is important to note that a general purpose computer that applies a judicial exception, such as an abstract idea, by use of conventional computer functions does not qualify as a particular machine (see MPEP 2106.05(b)).
In addition, Claim 16 recites the additional elements analyzed under Step 2A: prong 2 and Step 2B:
Claim 16: “storage; processing circuitry configured to execute instructions, that when executed, cause the processing circuitry to”- this storage and processing circuitry are recited at a high level of generality, and it is important to note that a general purpose computer that applies a judicial exception, such as an abstract idea, by use of conventional computer functions does not qualify as a particular machine (see MPEP 2106.05(b)).
Dependent claim(s) 3-8, 10-15, 17-21 when analyzed as a whole are held to be patent ineligible under 35 U.S.C. 101 because the additional recited limitation(s) fail(s) to establish that the claim(s) is/are not directed to an abstract idea. The claims are reciting further embellishment of the judicial exception.
Claim 3: this claim recites further embellishment about the mapping of the words and topics according to their contribution in a table format, therefore, it is able to be performed mentally with a pen and paper, and therefore, it qualifies as a mental process. It further recites the use of natural language inference method at a high level of generality, therefore, it amounts to mere instructions to apply a judicial exception on a computer (see MPEP 2106.05(f)).
Claim 4: this claim recites further embellishment about a third mapping of the words and topics according to their contribution, therefore, it is able to be performed mentally with a pen and paper, and therefore, it qualifies as a mental process. Further, it recites a final step of storing data of the result of the abstract idea, considered an insignificant extra solution activity per MPEP 2106.05 (g) under prong 2; and well-understood, routine, conventional activity under Step 2B.
Claim 5: this claim recites summing the contribution value of each word for each topic, therefore, it is able to be performed mentally with a pen and paper, and therefore, it qualifies as a mental process.
Claim 6: this claim recites further embellishment about the third mapping of the words and topics according to their contribution in a table format, therefore, it is able to be performed mentally with a pen and paper, and therefore, it qualifies as a mental process.
Claim 7: this claim recites selecting a model from a storage, which amounts to storing or retrieving information in memory, which has been recognized by the courts (as per Versata Dev. Group, Inc. v. SAP Am., Inc., 793 F.3d 1306, 1334, 115 USPQ2d 1681, 1701 (Fed. Cir. 2015)) as well-understood, routine, conventional activity (See MPEP 2106.05(d)(iv)) under step 2B.
Claim 8: this claim recites the training of the models being different, but this is recited at a high level of generality, which amounts to mere instructions to apply the judicial exception on a computer (see MPEP 2106.05(f)) under Step 2A, Prong 1.
Claim 9 is analogous to Claim 2, as explained above in the rejection of claim 2.
Claim 10 is analogous to Claim 3, therefore, same rationale and rejection applies.
Claim 11 is analogous to Claim 4, therefore, same rationale and rejection applies.
Claim 12 is analogous to Claim 5, therefore, same rationale and rejection applies.
Claim 13 is analogous to Claim 6, therefore, same rationale and rejection applies.
Claim 14 is analogous to Claim 7, therefore, same rationale and rejection applies.
Claim 15 is analogous to Claim 8, therefore, same rationale and rejection applies.
Claim 16 is analogous to Claim 2, as explained above in the rejection of claim 2.
Claim 17 is analogous to Claim 3, therefore, same rationale and rejection applies.
Claim 18 is analogous to Claim 4, therefore, same rationale and rejection applies.
Claim 19 is analogous to Claim 5, therefore, same rationale and rejection applies.
Claim 20 is analogous to Claim 6, therefore, same rationale and rejection applies.
Claim 21: this claims merely describes that the hierarchically organized components are words, and this does not amount to more than generally linking the use of a judicial exception to a particular technological environment or field of use, in this case, to the technology of topic management platform.
Viewed as a whole, these additional claim element(s) do not provide meaningful limitation(s) to transform the abstract idea into a patent eligible application of the abstract idea such that the claim(s) amounts to significantly more than the abstract idea itself. Therefore, the claim(s) are rejected under 35 U.S.C. 101 as being directed to non-statutory subject matter.
Claim Rejections - 35 USC § 103
In the event the determination of the status of the application as subject to AIA 35 U.S.C. 102 and 103 (or as subject to pre-AIA 35 U.S.C. 102 and 103) is incorrect, any correction of the statutory basis for the rejection will not be considered a new ground of rejection if the prior art relied upon, and the rationale supporting the rejection, would be the same under either status.
The following is a quotation of 35 U.S.C. 103 which forms the basis for all obviousness rejections set forth in this Office action:
A patent for a claimed invention may not be obtained, notwithstanding that the claimed invention is not identically disclosed as set forth in section 102, 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.
The factual inquiries for establishing a background for determining obviousness under 35 U.S.C. 103 are summarized as follows:
1. Determining the scope and contents of the prior art.
2. Ascertaining the differences between the prior art and the claims at issue.
3. Resolving the level of ordinary skill in the pertinent art.
4. Considering objective evidence present in the application indicating obviousness or nonobviousness.
This application currently names joint inventors. In considering patentability of the claims the examiner presumes that the subject matter of the various claims was commonly owned as of the effective filing date of the claimed invention(s) absent any evidence to the contrary. Applicant is advised of the obligation under 37 CFR 1.56 to point out the inventor and effective filing dates of each claim that was not commonly owned as of the effective filing date of the later invention in order for the examiner to consider the applicability of 35 U.S.C. 102(b)(2)(C) for any potential 35 U.S.C. 102(a)(2) prior art against the later invention.
Claims 2-6, 9-13, 16-21 are rejected under 35 U.S.C. 103 as being unpatentable over Deurloo (US PG Pub. 2013/0275527- hereinafter Deurloo) in view of Spencer (US Patent 5,915,249- hereinafter Spencer), and further in view of Mo et al NPL: “Supporting systematic reviews using LDA-based document representations”- hereinafter Mo).
Referring to Claim 2, Deurloo teaches a method, comprising:
determining a first corpus of documents and a first model to apply to the first corpus (see Deurloo at [0044]: “The visualization engine generates the Dynamic Squarified Treemaps. In the previous section, we have identified the major ingredients for building a squarified treemap. First, we have determined the variable vn, which represents the twitter speed in tweets per second on a particular topic. Second, we have identified the acceleration wt of the number of tweets for the same topic. Based on this information, we can build rectangles for each topic of which the relative areas correspond to the relative speeds of each topic”. Therefore, this Dynamic Squarified Treemap corresponds to the first model, and the tweets it is being applied to correspond to the first corpus of documents);
iteratively applying the first model to the first corpus to identify topics in the first corpus of documents, each topic associated with one or more words (see Deurloo at [0031]: “The system and methods disclosed herein use a Dynamic Squarified Treemap (see FIGS. 6A and 6B) to overcome all three aforementioned shortcomings. Advantageously, the importance of a topic can then be correlated to the size of a rectangle. The color of the rectangle can be used to identify if the topic is trending upwards, downwards or remaining at a steady popularity”. Further at [0051]: “Capture of the trending data by the system could, e.g., be based on the size and rate of growth of a cluster of words/topics. The dynamic squarified treemap can then serve as an early warning system in which topics that can become a trend can be visualized”. Further at [0054]: “the trending acceleration of the respective trending topic at the time point depends on a difference between the trending speed of the respective trending topic at the time point and a trending speed of the respective trending topic at a next time point”. Therefore, this Dynamic Squarified Treemap applied to the tweets for identifying trending topics based on words and identifying acceleration of the trends based on multiple timestamps corresponds to iteratively applying the model to identify topics);
determining, for each topic, contribution values for the one or more words to the topic, wherein each contribution value corresponds to one of the one or more words (see Deurloo at [0051]: “Capture of the trending data by the system could, e.g., be based on the size and rate of growth of a cluster of words/topics”. Therefore, the size of the growth corresponds to the contribution values),
generating a first map comprising the one or more words, the contribution values, and the topics, wherein each word of the one or more words is mapped to a particular topic and has a corresponding contribution value assigned thereto in the first map (see Deurloo at [0050]: “FIG. 6B shows the squarified treemap for the clustered topics. In some other embodiments, treemap can have shapes other than squares. The clusters in the FIG. 6B shows that `Azcapotzalco` is related to the earthquake in Mexico” and see [0051]: “Capture of the trending data by the system could, e.g., be based on the size and rate of growth of a cluster of words/topics. The dynamic squarified treemap can then serve as an early warning system in which topics that can become a trend can be visualized”. Therefore, the this trend visualization in a treemap is interpreted as the first map); and
storing, in storage, the first map (see Deurloo at [0056]: “At step 750, the system presents a treemap including multiple shapes for visualizing the trending speeds and trending accelerations of the trending topics. Each respective shape of the shapes is associated with a corresponding trending topic of the trending topics. An area of the respective shape indicates a trending speed of the corresponding trending topic at the time point. A color (or another property in some other embodiments) of the respective shape indicates a trending acceleration of the corresponding trending topic at the time point.”. Therefore, the since multiple time points are shown, the maps are stored for further comparison of the trends in the future);
comparing the first map to a stored second map to identify a newly trending topic (see Deurloo at [0048]: “Clustering can also be based on tweet list comparison. As a first clustering algorithm the system may adopt a very simple but efficient clustering algorithm. For each topic a, at time t, the system keeps a list la of the last 100 tweets counting back from time t. The system's similarity metric for topic a and topic b is defined as the number of times that both terms a and b appear in the lists l.sub.a and l.sub.b. If the similarity metric is above the threshold of 0.15, then the two topics are clustered”. Therefore, the comparison of a new tweet against a list of previous tweets (interpreted as a stored second map) is analogous to the claim language. Furthermore, see [0050]: “FIG. 6B shows the squarified treemap for the clustered topics. In some other embodiments, treemap can have shapes other than squares. The clusters in the FIG. 6B shows that `Azcapotzalco` is related to the earthquake in Mexico” and see [0051]: “Capture of the trending data by the system could, e.g., be based on the size and rate of growth of a cluster of words/topics. The dynamic squarified treemap can then serve as an early warning system in which topics that can become a trend can be visualized”. Therefore, the comparison of new tweet with a list of previous tweets for developing a treemap to recognize and alert of trends by providing a visualization is further interpreted as the identification of a newly trending topic); and
launching an early warning message system to issue a warning message indicating the identified newly trending topic (see Deurloo at [0050]: “The treemap can also be made dynamic by updating it in an animated manner periodically. This has the visual advantage of seeing the rectangles grow/shrink and change its color”. Further at [0051]: “Capture of the trending data by the system could, e.g., be based on the size and rate of growth of a cluster of words/topics. The dynamic squarified treemap can then serve as an early warning system in which topics that can become a trend can be visualized”. Therefore, the comparison of tweet with a list of previous tweets for developing a treemap to recognize and alert of trends by providing a visualization is further interpreted as the early warning message).
However, it fails to teach:
and each contribution value indicates a frequency one of the one or more words contributes to the topic; and
wherein the stored second map is generated based on applying a second model to a second corpus of documents wherein the second model is trained such that the second model reflects baseline topics in the second corpus, wherein the second model is applied to the second corpus of documents before the first model is applied to the first corpus.
Spencer teaches, in an analogous system, each contribution value indicates a frequency one of the one or more words contributes to the topic (see Spencer at Abstract “a plurality of documents ordered by a contribution that the term makes to the document score of the document. The contribution is a scalar measure of the influence of the term in the computed document score. The contribution reflects both the within document frequency and the between document frequency of the term”, and Col. 11: lines 21-27: “Alternatively, all of the contribution values for a term may be determined first, ranked, and then the n documents with the highest contribution values selected. The preprocess method then creates the lookup table 214 for the term by traversing the blocks of the term row in inverted index 200 and storing the (document, pointer) pair 213 information for the appropriate blocks”. Therefore, this determination of contribution value of each term in a document is interpreted as the contribution value of each word as claimed).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify the teachings of Deurloo with the above teachings of Spencer by processing a corpus using a machine learning model to identify a topic within the corpus, as taught by Deurloo, and determine a contribution value of each word in the corpus, as taught by Spencer. The modification would have been obvious because one of ordinary skill in the art would be motivated to determine a scalar value of the influence of a term in a corpus of documents (as suggested by Spencer at [Abstract]: “The contribution is a scalar measure of the influence of the term in the computed document score. The contribution reflects both the within document frequency and the between document frequency of the term”).
Mo teaches, in an analogous system,
wherein the stored second map is generated based on applying a second model to a second corpus of documents wherein the second model is trained such that the second model reflects baseline topics in the second corpus, wherein the second model is applied to the second corpus of documents before the first model is applied to the first corpus (see Mo at p. 3: Experimental design: “Firstly, we evaluate the baseline approach, i.e. an SVM using BOW features. This SVM classifier is created using LIBSVM[37].The second part of the experiment involves applying LDA for modelling topic distribution in the datasets, followed by the training of an SVM-based classifier using the topic distribution as features”. Therefore, the SVM is interpreted as the second model as it is the baseline, and the LDA corresponds to the first model as it follows the use of the SVM, being the second model).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify the combination of Deurloo and Spencer with the above teachings of Mo by processing a corpus to identify a topic within the corpus and determine a contribution value of each word in the corpus, as taught by Deurloo and Spencer, and using two models to have a baseline, as taught by Mo. The modification would have been obvious because one of ordinary skill in the art would be motivated to compare performance of models to provide more informative and less ambiguous results to viewers (as suggested by Mo at [Abstract]: “A topic-based feature representation of documents outperforms the BOW representation when applied to the task of automatic citation screening. The proposed term-enriched topics are more informative and less ambiguous to systematic reviewers”).
Referring to Claim 3, the combination of Deurloo, Spencer and Mo teaches the method of claim 2, comprising displaying, the first map in a table format illustrating the one or more words and their contribution values to each topic (see Spencer at Col. 11: lines 21-27: “Alternatively, all of the contribution values for a term may be determined first, ranked, and then the n documents with the highest contribution values selected. The preprocess method then creates the lookup table 214 for the term by traversing the blocks of the term row in inverted index 200 and storing the (document, pointer) pair 213 information for the appropriate blocks”. Therefore, this created lookup table is interpreted as the claimed map in table format);
selecting a topic label for at least one of the topics using a natural language inference method (see Deurloo at [0016]: “In order to use this visual representation, the system needs to determine (preferably forecast) the speed at which tweets on a particular subject are posted and to detect acceleration. Moreover, the system needs efficient ways to relate topics to each other when necessary so that clusters of related trending topics are formed to be more informative about a particular subject. Methodologies for determining the speed and acceleration, and for clustering are described herein”. Therefore, the topic subject determination is interpreted as the topic label); and
applying the topic label to the first corpus (see Deurloo at [0016]: “In order to use this visual representation, the system needs to determine (preferably forecast) the speed at which tweets on a particular subject are posted and to detect acceleration. Moreover, the system needs efficient ways to relate topics to each other when necessary so that clusters of related trending topics are formed to be more informative about a particular subject. Methodologies for determining the speed and acceleration, and for clustering are described herein”. Therefore, the topic subject determined and clustered is interpreted as the topic label applied to the corpus, being the tweets).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify the teachings of Deurloo with the above teachings of Spencer by processing a corpus to identify a topic within the corpus, as taught by Deurloo, and determine a contribution value of each word in the corpus, as taught by Spencer. The modification would have been obvious because one of ordinary skill in the art would be motivated to determine a scalar value of the influence of a term in a corpus of documents (as suggested by Spenser at [Abstract]).
Referring to Claim 4, the combination of Deurloo, Spencer and Mo teaches the method of claim 2, comprising: generating a third map comprising, for each of the documents of the first corpus, a relative distribution of the one or more words per topic per document; and storing, in the storage, the third map (see Spencer at Col. 11: lines 14-27: “For computational efficiency the documents are ranked by contribution as the contributions are being determined, for example in any of a variety of tree data structures, such as AVL trees, splay trees, or the like, for storing the n highest ranking documents by contribution. Once all documents in the term row are processed, the n tuples 208 are stored from this data. Alternatively, all of the contribution values for a term may be determined first, ranked, and then the n documents with the highest contribution values selected. The preprocess method then creates the lookup table 214 for the term by traversing the blocks of the term row in inverted index 200 and storing the (document, pointer)”. Therefore, the n documents stored, each having look up tables based on their contribution, is interpreted as the third map stored).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify the teachings of Deurloo with the above teachings of Spencer by processing a corpus to identify a topic within the corpus, as taught by Deurloo, and determine a contribution value of each word in the corpus, as taught by Spencer. The modification would have been obvious because one of ordinary skill in the art would be motivated to determine a scalar value of the influence of a term in a corpus of documents (as suggested by Spencer at [Abstract]).
Referring to Claim 5, the combination of Deurloo, Spencer and Mo teaches the method of claim 4, comprising determining the relative distribution by summing the contribution value of each word of the document for each topic (see Spencer at Col. 11: lines 21-27: “Alternatively, all of the contribution values for a term may be determined first, ranked, and then the n documents with the highest contribution values selected. The preprocess method then creates the lookup table 214 for the term by traversing the blocks of the term row in inverted index 200 and storing the (document, pointer) pair 213 information for the appropriate blocks”. Therefore, since the contribution values are ranked for each term, this interpreted as summing).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify the teachings of Deurloo with the above teachings of Spencer by processing a corpus to identify a topic within the corpus, as taught by Deurloo, and determine a contribution value of each word in the corpus, as taught by Spencer. The modification would have been obvious because one of ordinary skill in the art would be motivated to determine a scalar value of the influence of a term in a corpus of documents (as suggested by Spencer at [Abstract]).
Referring to Claim 6, the combination of Deurloo, Spencer and Mo teaches the method of claim 4, comprising displaying, the second map in a table format (see Spencer at Col. 11: lines 21-27: “Alternatively, all of the contribution values for a term may be determined first, ranked, and then the n documents with the highest contribution values selected. The preprocess method then creates the lookup table 214 for the term by traversing the blocks of the term row in inverted index 200 and storing the (document, pointer) pair 213 information for the appropriate blocks”. Therefore, this created lookup table is interpreted as the claimed map in table format).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify the teachings of Deurloo with the above teachings of Spencer by processing a corpus to identify a topic within the corpus, as taught by Deurloo, and determine a contribution value of each word in the corpus, as taught by Spencer. The modification would have been obvious because one of ordinary skill in the art would be motivated to determine a scalar value of the influence of a term in a corpus of documents (as suggested by Spencer at [Abstract]).
Referring to independent Claim 9, it is rejected on the same basis as independent claim 2 since they are analogous claims.
Referring to dependent Claim 10, it is rejected on the same basis as dependent claim 3 since they are analogous claims.
Referring to dependent Claim 11, it is rejected on the same basis as dependent claim 4 since they are analogous claims.
Referring to dependent Claim 12, it is rejected on the same basis as dependent claim 5 since they are analogous claims.
Referring to dependent Claim 13, it is rejected on the same basis as dependent claim 6 since they are analogous claims.
Referring to Claim 16, Deurloo teaches a system comprising:
storage (see Deurloo at [0060]: “While the machine-readable (storage) medium is shown in an exemplary embodiment to be a single medium, the term "machine-readable (storage) medium" should be taken to include a single medium or multiple media”);
processing circuitry configured to execute instructions, that when executed, cause the processing circuitry to (see Deurloo at [0061]: “The computer programs typically comprise one or more instructions set at various times in various memory and storage devices in a computer, and that, when read and executed by one or more processors in a computer, cause the computer to perform operations to execute elements involving the various aspects of the disclosure”):
determine a first corpus of documents and a first model to apply to the first corpus (see Deurloo at [0044]: “The visualization engine generates the Dynamic Squarified Treemaps. In the previous section, we have identified the major ingredients for building a squarified treemap. First, we have determined the variable vn, which represents the twitter speed in tweets per second on a particular topic. Second, we have identified the acceleration wt of the number of tweets for the same topic. Based on this information, we can build rectangles for each topic of which the relative areas correspond to the relative speeds of each topic”. Therefore, this Dynamic Squarified Treemap corresponds to the first model, and the tweets it is being applied to correspond to the first corpus of documents);
iteratively apply the first model to the first corpus to identify topics in the first corpus of documents, each topic comprising a plurality of hierarchically organized components (see Deurloo at [0031]: “The system and methods disclosed herein use a Dynamic Squarified Treemap (see FIGS. 6A and 6B) to overcome all three aforementioned shortcomings. Advantageously, the importance of a topic can then be correlated to the size of a rectangle. The color of the rectangle can be used to identify if the topic is trending upwards, downwards or remaining at a steady popularity”. Further at [0051]: “Capture of the trending data by the system could, e.g., be based on the size and rate of growth of a cluster of words/topics. The dynamic squarified treemap can then serve as an early warning system in which topics that can become a trend can be visualized”. Further at [0054]: “the trending acceleration of the respective trending topic at the time point depends on a difference between the trending speed of the respective trending topic at the time point and a trending speed of the respective trending topic at a next time point”. Therefore, this Dynamic Squarified Treemap applied to the tweets for identifying trending topics based on words and identifying acceleration of the trends based on multiple timestamps corresponds to iteratively applying the model to identify topics);
determine, for each topic, contribution values for the plurality of hierarchically organized components to the topic, wherein each contribution value corresponds to one of the hierarchically organized components (see Deurloo at [0051]: “Capture of the trending data by the system could, e.g., be based on the size and rate of growth of a cluster of words/topics”. Therefore, the size of the growth corresponds to the contribution values),
determine a first map comprising the plurality of hierarchically organized components, the contribution values, and the topics, wherein each hierarchically organized component is mapped to a particular topic and has a corresponding contribution value assigned thereto in the first map (see Deurloo at [0050]: “FIG. 6B shows the squarified treemap for the clustered topics. In some other embodiments, treemap can have shapes other than squares. The clusters in the FIG. 6B shows that `Azcapotzalco` is related to the earthquake in Mexico” and see [0051]: “Capture of the trending data by the system could, e.g., be based on the size and rate of growth of a cluster of words/topics. The dynamic squarified treemap can then serve as an early warning system in which topics that can become a trend can be visualized”. Therefore, the this trend visualization in a treemap is interpreted as the first map); and
store, in the storage, the first map (see Deurloo at [0056]: “At step 750, the system presents a treemap including multiple shapes for visualizing the trending speeds and trending accelerations of the trending topics. Each respective shape of the shapes is associated with a corresponding trending topic of the trending topics. An area of the respective shape indicates a trending speed of the corresponding trending topic at the time point. A color (or another property in some other embodiments) of the respective shape indicates a trending acceleration of the corresponding trending topic at the time point.”. Therefore, the since multiple time points are shown, the maps are stored for further comparison of the trends in the future);
compare the first map to a stored second map to identify a newly trending topic (see Deurloo at [0048]: “Clustering can also be based on tweet list comparison. As a first clustering algorithm the system may adopt a very simple but efficient clustering algorithm. For each topic a, at time t, the system keeps a list la of the last 100 tweets counting back from time t. The system's similarity metric for topic a and topic b is defined as the number of times that both terms a and b appear in the lists l.sub.a and l.sub.b. If the similarity metric is above the threshold of 0.15, then the two topics are clustered”. Therefore, the comparison of a new tweet against a list of previous tweets (interpreted as a stored second map) is analogous to the claim language. Furthermore, see [0050]: “FIG. 6B shows the squarified treemap for the clustered topics. In some other embodiments, treemap can have shapes other than squares. The clusters in the FIG. 6B shows that `Azcapotzalco` is related to the earthquake in Mexico” and see [0051]: “Capture of the trending data by the system could, e.g., be based on the size and rate of growth of a cluster of words/topics. The dynamic squarified treemap can then serve as an early warning system in which topics that can become a trend can be visualized”. Therefore, the comparison of new tweet with a list of previous tweets for developing a treemap to recognize and alert of trends by providing a visualization is further interpreted as the identification of a newly trending topic); and
launch an early warning message system to issue a warning message indicating the identified newly trending topic (see Deurloo at [0050]: “The treemap can also be made dynamic by updating it in an animated manner periodically. This has the visual advantage of seeing the rectangles grow/shrink and change its color”. Further at [0051]: “Capture of the trending data by the system could, e.g., be based on the size and rate of growth of a cluster of words/topics. The dynamic squarified treemap can then serve as an early warning system in which topics that can become a trend can be visualized”. Therefore, the comparison of tweet with a list of previous tweets for developing a treemap to recognize and alert of trends by providing a visualization is further interpreted as the early warning message).
However, Deurloo fails to teach:
and each contribution value indicates a frequency one of the plurality of hierarchically organized components contributes to the topic; and
wherein the stored second map is generated based on applying a second model to a second corpus of documents, wherein the second model is trained such that the second model reflects baseline topics in the second corpus, wherein the second model is applied to the second corpus before the first model is applied to the first corpus.
Spencer teaches, in an analogous system, each contribution value indicates a frequency one of the one or more words contributes to the topic (see Spencer at Abstract “a plurality of documents ordered by a contribution that the term makes to the document score of the document. The contribution is a scalar measure of the influence of the term in the computed document score. The contribution reflects both the within document frequency and the between document frequency of the term”, and Col. 11: lines 21-27: “Alternatively, all of the contribution values for a term may be determined first, ranked, and then the n documents with the highest contribution values selected. The preprocess method then creates the lookup table 214 for the term by traversing the blocks of the term row in inverted index 200 and storing the (document, pointer) pair 213 information for the appropriate blocks”. Therefore, this determination of contribution value of each term in a document is interpreted as the contribution value of each word as claimed).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify the teachings of Deurloo with the above teachings of Spencer by processing a corpus using a machine learning model to identify a topic within the corpus, as taught by Deurloo, and determine a contribution value of each word in the corpus, as taught by Spencer. The modification would have been obvious because one of ordinary skill in the art would be motivated to determine a scalar value of the influence of a term in a corpus of documents (as suggested by Spencer at [Abstract]: “The contribution is a scalar measure of the influence of the term in the computed document score. The contribution reflects both the within document frequency and the between document frequency of the term”).
Mo teaches, in an analogous system,
wherein the stored second map is generated based on applying a second model to a second corpus of documents, wherein the second model is trained such that the second model reflects baseline topics in the second corpus, wherein the second model is applied to the second corpus before the first model is applied to the first corpus (see Mo at p. 3: Experimental design: “Firstly, we evaluate the baseline approach, i.e. an SVM using BOW features. This SVM classifier is created using LIBSVM[37].The second part of the experiment involves applying LDA for modelling topic distribution in the datasets, followed by the training of an SVM-based classifier using the topic distribution as features”. Therefore, the SVM is interpreted as the second model as it is the baseline, and the LDA corresponds to the first model as it follows the use of the SVM, being the second model).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify the combination of Deurloo and Spencer with the above teachings of Mo by processing a corpus to identify a topic within the corpus and determine a contribution value of each word in the corpus, as taught by Deurloo and Spencer, and using two models to have a baseline, as taught by Mo. The modification would have been obvious because one of ordinary skill in the art would be motivated to compare performance of models to provide mor informative and less ambiguous results to viewers (as suggested by Mo at [Abstract]: “A topic-based feature representation of documents outperforms the BOW representation when applied to the task of automatic citation screening. The proposed term-enriched topics are more informative and less ambiguous to systematic reviewers”).
Referring to Claim 17, it is rejected on the same basis as dependent claim 3 since they are analogous claims.
Referring to Claim 18, it is rejected on the same basis as dependent claim 4 since they are analogous claims.
Referring to Claim 19, it is rejected on the same basis as dependent claim 5 since they are analogous claims.
Referring to Claim 20, it is rejected on the same basis as dependent claim 6 since they are analogous claims.
Referring to Claim 21, the combination of Deurloo, Spencer and Mo teaches the system of claim 16, wherein the plurality of hierarchically organized components comprises words (see Deurloo at [0051]: “Capture of the trending data by the system could, e.g., be based on the size and rate of growth of a cluster of words/topics”).
Claims 7 and 14 are rejected under 35 U.S.C. 103 as being unpatentable over Deurloo (US PG Pub. 2013/0275527- hereinafter Deurloo) in view of Spencer (US Patent 5,915,249- hereinafter Spencer), in view of Mo et al NPL: “Supporting systematic reviews using LDA-based document representations”- hereinafter Mo), and further in view of Eder et al (US Pub. No. 2015/0235143- hereinafter Eder).
Referring to Claim 7, the combination of Deurloo, Spencer and Mo teaches the method of claim 2, however, fails to teach wherein the first model is selected from a plurality of first models stored in the storage, and the plurality of first models are trained using a plurality of different corpora.
Eder teaches, in an analogous system, wherein the model is selected from a plurality of models stored in the storage, and the plurality of models are trained using a plurality of different corpora (see Eder at [0284]: “A software block 308 then uses "bootstrapping" where different training data sets are created by re-sampling with replacement from the original training set so data records may occur more than once”, and [0286]- “[a]fter the causal predictive model bots complete their training for each model, the software in block 309 uses a model selection algorithm to identify the model that best fits the data. For the system of the present embodiment, a cross validation algorithm (e.g., the tenfold cross validation algorithm) is used for model selection”).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify the combination of Deurloo, Spencer and Mo with the above teachings of Eder by processing a corpus using a plurality of machine learning models program code to identify a topic within the corpus, as taught by Deurloo, Spencer and Mo, and selecting a machine learning model from the plurality of models, as taught by Eder. The modification would have been obvious because one of ordinary skill in the art would be motivated to use a model selection algorithm to identify the model that best fits the data (as suggested by Eder at [0286]).
Referring to dependent Claim 14, it is rejected on the same basis as dependent claim 7 since they are analogous claims.
Claims 8 and 15 are rejected under 35 U.S.C. 103 as being unpatentable over Deurloo (US PG Pub. 2013/0275527- hereinafter Deurloo) in view of Spencer (US Patent 5,915,249- hereinafter Spencer), in view of Mo et al NPL: “Supporting systematic reviews using LDA-based document representations”- hereinafter Mo), in view of Eder et al (US Pub. No. 2015/0235143- hereinafter Eder), and further in view of Tapia et al (US 10,063,406- hereinafter Tapia).
Referring to Claim 8, the combination of Deurloo, Spencer, Mo and Eder teaches the method of claim 7, however, fails to teach wherein the plurality of different corpora comprise corpora captured from different time periods, different sources, or a combination thereof
Tapia teaches, in an analogous system, wherein the plurality of different corpora comprise corpora captured from different time periods, different sources, or a combination thereof (see Tapia at Column 8: 54-58; “aggregate data from multiple data sources for a particular time period into an aggregated data file of data sets according to one or more grouping parameters. The grouping parameters may include specific time periods (e.g., hourly, daily, etc.),”. Moreover, Tapia at Column 19: 43-47 “[i]n various embodiments, the network cell condition information for a geolocation may include the amount of network bandwidth available at different times in a daily cycle, the signal spectrums of the network cell that are utilized at different times” and lines 60-67: “For example, data usage by some of the subscribers who engaged in excess data usage negatively affected network bandwidth availability at a network cell because the usage occurred at peak times. However, data usage by other subscribers with excess usage may have occurred at non-peak times and therefore did not negatively affect network bandwidth availability at the network cell”. Therefore, the training data contains data from different times of the day and is aggregated when needed for a particular time period).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify the combination of Deurloo, Spencer, Mo and Eder with the above teachings of Tapia by processing a corpus using a plurality of machine learning models program code to identify a topic within the corpus and selecting a machine learning model from the plurality of models, as taught by Deurloo, Spencer, Mo, and Eder, and using different time period and corpora in the training, as taught by Tapia. The modification would have been obvious because one of ordinary skill in the art would be motivated to include data from different times of the day in the different corpora for the models trained by Eder, as this will bring more information for the models such as peak and non-peak times relevant data events of issues.
Referring to dependent Claim 15, it is rejected on the same basis as dependent claim 8 since they are analogous claims.
Response to Arguments
Applicant's arguments filed 04/15/2026 have been fully considered.
In reference to Applicant’s arguments:
- Double patenting rejections.
Examiner’s response:
Rejections under non-statutory double patenting rejection are maintained under obviousness analysis, as explained above at the beginning of this office action.
In reference to Applicant’s arguments:
- Claim rejections under 35 USC 101.
Examiner’s response:
Applicant asserts that Claim 2 (and independent claims 9 and 16 as being analogous) is patent eligible under 35 USC 101, however, Examiner respectfully disagrees. Applicant’s main arguments are directed to the newly added limitation at independent claims 2, 9, and 16, asserting that they are not directed to a judicial exception under Prong 1, and that further provides an improvement by performing an iterative comparison between a "first map" and a stored "second map" (the baseline), the system provides a technical solution to the problem of baseline noise in large datasets. Applicant further asserts that this high-fidelity comparison allows the computer to isolate "newly trending topics" with a level of precision and scale that is physically impossible for the human mind to achieve, and consequently, the claim is not merely "observing" a trend; it is utilizing a specific, non-conventional data structure to improve the accuracy and speed of automated trend identification. Examiner respectfully disagrees. Examiner do recognizes that this limitation does not recite an Abstract idea under Prong 1, however, it is evaluated under Prong 2 and Step 2B and it is considered as mere instructions to apply an exception on a computer, as it is reciting the use of two models, one as a baseline and one as an additional one, for the purpose of identifying topics (which is the recited abstract idea at Prong 1 above). As explained in the 35 USC 112 (a) rejection above, Examiner did not find in the Specification any explanation of a connection or relationship between these two models and two corpus. The language recited in the claim does not match the description in the Specification. In addition, based on the Specification, it seems there is a “champion” model and a “challenger” model, however, there is no specific improvement or benefit described so that a person having ordinary skill in the art can conclude that it may amount to a practical application or significantly more.
Applicant further asserts that dependent claim 3, as amended, provides an improvement that overcomes inconsistencies currently experienced in the art. Examiner respectfully disagrees, as Claim 3 merely recites the use of natural language to select a topic label, and paragraph [0023] recites “As will be described in more detail below, trained models may be stored for analysis of later-generated corpus, to identify changes in topic trends between corpus collected during different time periods and to correlate business events to trending topics”. Using natural language to identify a label, or a subject of the topic, is merely mere instructions to apply a judicial exception (mental process or recognizing a label or subject of a topic) on a computer. Rejections are still maintained.
In reference to Applicant’s arguments:
- Claim rejections under 35 USC 103.
Examiner’s response:
Applicant arguments have been fully considered but are moot in view of new grounds of rejection.
Conclusion
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/LUIS A SITIRICHE/Primary Examiner, Art Unit 2126
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