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 .
Remarks
This action is in response to the amendments received on 1/21/26. Claims 1-20 are pending in the application. Applicants' arguments have been carefully and respectfully considered.
Claims 1-20 are rejected on the ground of nonstatutory double patenting.
Claims 1-20 are rejected under 35 U.S.C. 101.
Claims 1-20 are rejected under 35 U.S.C. 103 as being unpatentable over Dredze, and further in view of Pattabiraman et al. (US 10,691,760) and Kumar et al. (US 2012/0310930).
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 1-20 are rejected on the ground of nonstatutory double patenting as being unpatentable over claims 1-4 and 10 of U.S. Patent No. 12,299,051. Although the claims at issue are not identical, they are not patentably distinct from each other because the earlier claims anticipate the current claims.
12,299,051
Current Application
A method, comprising: receiving a plurality of documents;
1. A computer implemented method for filtering documents based on a selected filter value, the method, comprising: receiving a plurality of documents;
receiving a selection of a pivot, the pivot being a field value or a set of field values for the plurality of documents;
receiving a selection of filter value, the filter value being a field value or a set of field values for the received plurality of documents;
generating a prediction of responsive phrases, responsive concepts, or other meta-data, the prediction based at least in part of evaluating the plurality of documents based on the pivot, the pivot serving as an input for the generating of the prediction of the responsive phrases, the responsive concepts, or the other meta-data;
generating a prediction of responsive phrases, responsive concepts, or other meta-data, the prediction based at least in part on evaluating the received plurality of documents based on the filter value, the filter value serving as an input for the generating of the prediction of the responsive phrases, the responsive concepts, or the other meta-data;
further evaluating the plurality of documents based on a new pivot, the new pivot being one of the predicted responsive phrases or the predicted responsive concepts;
further evaluating the received plurality of documents based on a new filter value, the new filter value being one of the predicted responsive phrases or predicted responsive concepts;
generating a prediction of other responsive phrases or other responsive concepts;
generating a prediction of other responsive phrases or other responsive concepts;
calculating a predictive value for each of the other predicted responsive phrases or the other predicted responsive concepts, the predictive value being indicative of a likelihood that the other predicted responsive phrases or the other predicted responsive concepts are to be associated with documents of the plurality of documents that are tagged with the pivot;
generating, using a machine learning model trained on historical document-tag associations, a predictive value for each of the other predicted responsive phrases or the other predicted responsive concepts,
generating a graphical user interface that comprises automatically generated filter criteria based on the predictive value for each of the other predicted responsive phrases, the other predicted responsive concepts, or the other predictive predicted meta-data, the automatically generated filter criteria being based on key phrases identified in a preview frame of the graphical user interface, the key phrases comprising selectable filter parameters;
generating a graphical user interface that comprises automatically generated filter criteria based on the predictive value for each of the other predicted responsive phrases, the other predicted responsive concepts, or the other predictive predicted meta-data, the automatically generated filter criteria being based on key phrases identified in a preview frame of the graphical user interface, the key phrases comprising selectable filter parameters;
receiving a selection of at least one of the filter criteria from the graphical user interface;
receiving a selection of at least one of the filter criteria from the graphical user interface;
building and applying a filter based on the selection; and
building and applying a filter based on the selection; and
displaying within the graphical user interface, documents from the plurality of documents that were selected, using the at least one of the filter criteria.
based on the filter, generating documents from at least a sub-portion of the received plurality of documents.
The patent does not claim “generating, using a machine learning model trained on historical document-tag associations, a predictive value for each of the other predicted responsive phrases or the other predicted responsive concepts.”
Dredze teaches generating, using a machine learning model trained on historical document-tag associations, a predictive value for each of the other predicted responsive phrases or the other predicted responsive concepts (Col. 13 Li. 4-14, the weight associated with each facet characteristic is based on historical popularity of presentation facets having the facet characteristics (618). In these embodiments, data is collected on which presentation facets users actually select compared to the predicted calculated ranking, and machine learning is used to adjust the weights to bring them more in line with actual usage. The machine learning can be performed in a testing environment, or in a production environment on an occasional, periodic or continual basis to improve selection of the presentation facets.).
It would have been obvious at the effective filing date of the invention to a person having ordinary skill in the art to which said subject matter pertains to have included the teachings of Dredze because it improves processing and uses preferences of users to increase usefulness of the returned data (Dredze, Col. 13 Li. 4-14 & 37-45).
Claim Rejections - 35 USC § 101
35 U.S.C. 101 reads as follows:
Whoever invents or discovers any new and useful process, machine, manufacture, or composition of matter, or any new and useful improvement thereof, may obtain a patent therefor, subject to the conditions and requirements of this title.
Claims 1-20 are rejected under 35 U.S.C. 101 because the claimed invention is directed to a judicial exception without significantly more.
Step 2A, Prong One asks: Is the claim directed to a law of nature, a natural phenomenon (product of nature) or an abstract idea? See MPEP 2106.04 Part I. If a claim limitation, under its broadest reasonable interpretation, covers certain methods of organizing human activity or performance of the limitation in the mind but for the recitation of generic computer components, then the claim recites an abstract idea. See MPEP 2106.04(a).
With respect to claims 1, 10, and 12, the limitation of “generating a prediction” and “further evaluating the received plurality of documents”, 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, nothing in the claim element precludes the step from practically being performed in the mind. For example, “generating” and “evaluating” in the context of this claim encompasses the user mentally analyzing data. Similarly, the limitation of “building and applying a filter” and “generating documents from at least a sub-portion of the received plurality of documents”, as drafted, is a process that, under its broadest reasonable interpretation, is a well-known method of organizing human activity but for the recitation of generic computer components. For example, “building and applying” and “generating” in the context of this claim encompasses the idea of filtering content to a parent or librarian forbidding children from reading certain books (See BASCOM Global Internet v. AT&T Mobility, LLC, 827 F.3d 1341, 1345-46, 119 USPQ2d 1236, 1239 (Fed. Cir. 2016)).
If the identified limitation(s) falls within at least one of the groupings of abstract ideas, it is reasonable to conclude that the claim recites an abstract idea in Step 2A Prong One. The claim then requires further analysis in Step 2A Prong Two, to determine whether any additional elements in the claim integrate the abstract idea into a practical application.
At step 2a, prong two, this judicial exception is not integrated into a practical application. Claim 12 recites a processor to execute the operations and a machine learning model, however, this is recited as a high-level of generality such that it amounts to no more than mere instructions to apply the exception using a generic computer component. Additionally, the claim recites “receiving a plurality of documents,” “receiving a selection of a filter value,” “receiving a selection of at least one of the filter criteria,” and “generating a graphical user interface that comprises automatically generated filter criteria based on the predictive value for each of the other predicted responsive phrases, the other predicted responsive concepts, or the other predicted meta-data.” These elements do not integrate the abstract idea into a practical application because they do not impose a meaningful limit on the judicial exception and provide only insignificant extra solution activity that is mere data gathering in conjunction with the abstract idea.
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 amount to no more than mere instructions to apply an exception using generic computer components. Mere instructions to apply an exception using generic computer components cannot provide an inventive concept.
With respect to “receiving a plurality of documents,” “receiving a selection of a filter value,” and “receiving a selection of at least one of the filter criteria”, the courts have found limitations directed towards data gathering to be well-understood, routine, and conventional. See MPEP 2106.05(d)(II). 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).
With respect to “generating a graphical user interface”, the courts have found limitations directed towards obtaining information electronically to be well-understood, routine, and conventional. See MPEP 2106.05(d)(II). Presenting offers and gathering statistics, OIP Techs., 788 F.3d at 1362-63, 115 USPQ2d at 1092-93.
Considering the additional elements individually and in combination and the claim as a whole, the additional elements do not provide significantly more than the abstract idea. The claim is not patent eligible.
With respect to claims 2-4 and 13-15, the limitations further define the above components and do not include additional elements.
With respect to claims 5 and 16, the limitations disclose updating the filter value and rebuilding the filter. Under its broadest reasonable interpretation, this covers performance of the limitation in the mind but for the recitation of generic computer components. That is, nothing in the claim element precludes the step from practically being performed in the mind. For example, “updating” and “rebuilding” in the context of this claim encompasses the user mentally analyzing data.
With respect to claims 6 and 17, the limitations further define the above components and do not include additional elements.
With respect to claims 7, 9, 11, 18, and 20, the limitations disclose “generating a predictive value”, which has been addressed above.
With respect to claims 8 and 19, the limitations disclose “determining a frequency count value” which 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, nothing in the claim element precludes the step from practically being performed in the mind. For example, “determining” in the context of this claim encompasses the user mentally analyzing data.
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 1-20 are rejected under 35 U.S.C. 103 as being unpatentable over Dredze, and further in view of Pattabiraman et al. (US 10,691,760) and Kumar et al. (US 2012/0310930).
With respect to claim 1, Dredze teaches a method, comprising:
receiving a plurality of documents (Dredze, Fig. 6a, step 604 & Col. 9 Li. 14-16, Process flow 500 begins with a set of search results 502, which is generated by Query Module 116 in response to a user's query.);
receiving a selection of a filter value, the filter value being a field value or a set of field values for the received plurality of documents (Dredze, Col. 7 Li. 12-16, search query could include a "PDF type" facet that specifies that the documents sought are Adobe® Acrobat® files (PDF), e.g., by including the search facet "type:PDF". This example facet comprises the operator "type:" and the operand "PDF". & Col. 8 Li. 16-27, fields in documents);
generating a prediction of responsive phrases, responsive concepts, or other meta-data, the prediction based at least in part on evaluating the received plurality of documents based on the filter value, the filter value serving as an input for the generating of the prediction of the responsive phrases, the responsive concepts, or the other meta-data (Dredze, Col. 9 Li. 14-19, Process flow 500 begins with a set of search results 502, which is generated by Query Module 116 in response to a user's query. Using the initial set of search results 502, and the operator list 222 (e.g., as shown in FIG. 3), the Facet Generation Module 118 generates (504) candidate facets 224);
further evaluating the received plurality of documents based on a new filter value, the new filter value being one of the predicted responsive phrases or predicted responsive concepts (Dredze, Fig. 6b, step 626 & Col. 13 Li. 62-67, First, the Control Module 112 creates (626) a revised search query comprising the initial search query and the selected presentation facet. In some embodiments the revised search query is the concatenation of the text string of the initial query and a text string corresponding to the selected presentation facet);
generating a prediction of other responsive phrases or other responsive concepts (Dredze, Fig. 6a & 6b, “B” represents the loop between user selection step 624 & generating new facets in step 608 & Col. 14 Li. 11-14, After the revised set of search results is generated, Facet Generation Module 118 generates (608) a new set of candidate facets, and proceeds in the same way as processing the initial search query.);
generating, using a machine learning model trained on historical document-tag associations, a predictive value for each of the other predicted responsive phrases or the other predicted responsive concepts (Dredze, Col. 13 Li. 4-14, the weight associated with each facet characteristic is based on historical popularity of presentation facets having the facet characteristics (618). In these embodiments, data is collected on which presentation facets users actually select compared to the predicted calculated ranking, and machine learning is used to adjust the weights to bring them more in line with actual usage. The machine learning can be performed in a testing environment, or in a production environment on an occasional, periodic or continual basis to improve selection of the presentation facets. & Fig. 6a, step 610 & Col. 9 Li. 45-52, Based on the facet list 224 (which includes the characteristic vectors), and the weights of the facet characteristics 228, Facet Ranking Module 120 ranks (516) the candidate facets to create a ranked list of candidate facets 518…the rank of each candidate facet is computed as the vector dot product of the weights w and the characteristic vector v, namely w·v.);
generating a graphical user interface that comprises automatically generated filter criteria based on the predictive value for each of the other predicted responsive phrases, the other predicted responsive concepts, or the other predicted meta-data (Dredze, Col. 13 Li. 15-21, The Facet Ranking Module 120, Control Module 112 or User Interface Module 114 selects (620) a plurality of presentation facets from among the candidate facets in accordance with the rankings of the candidate facets. In some embodiments, the selection (620) takes the top R candidate facets based on the ranking, where R is the number of facets that can be displayed to a user.);
receiving a selection of at least one of the filter criteria (Dredze, Col. 13 Li. 59-62, A user may select any of the presentation facets once they are displayed);
building and applying a filter based on the selection (Dredze, Col. 13 Li. 62-67, First, the Control Module 112 creates (626) a revised search query comprising the initial search query and the selected presentation facet. In some embodiments the revised search query is the concatenation of the text string of the initial query and a text string corresponding to the selected presentation facet); and
based on the filter, generating documents from at least a sub-portion of the received plurality of documents (Dredze, Col. 14 Li. 1-2, the Query Module 116 generates (628) a revised set of search results based on the revised search query.).
Dredze doesn't expressly discuss the predictive value being computed based on a statistical correlation metric selected from chi-squared statistic or pointwise mutual information and automatically generated filter criteria being based on key phrases identified in a preview frame of the graphical user interface, the key phrases comprising selectable filter parameters.
Pattabiraman teaches generating a graphical user interface that comprises automatically generated filter criteria based on the predictive value for each of the other predicted responsive phrases, the other predicted responsive concepts, or the other predicted meta-data (Pattabiraman, Fig. 3, 4 & Col. 4 Li. 29-35, a suggestion facet user interface presents a user with a set of one or more filters (also termed facets). For example, with respect to searches that have been identified as related to jobs posted in an online network system, facets that may be used to refine search results may include a "company" facet, a "location" facet, etc.), the automatically generated filter criteria being based on key phrases identified in a preview frame of the graphical user interface, the key phrases comprising selectable filter parameters (Pattabiraman, Col. 4 Li. 65- Col. 5 Li. 28, given a set of candidate suggestions ( or facet-value pairs) that may each be provided to a searcher (e.g. , via a suggestion facet user interface) the guided search system 200 may assign a
popularity score to each of the candidate suggestions … The guided search system 200 may then rank each of the candidate suggestions, based on their popularity scores, and select the highest ranked candidate suggestion for insertion into a suggestion facet user interface.);
receiving a selection of at least one of the filter criteria; building and applying a filter based on the selection; and based on the filter, generating documents from at least a sub-portion of the received plurality of documents (Pattabiraman, Col. 4 Li. 41-45, If the user clicks on the suggestion facet user interface, it will filter all the search results (above and below the suggestion facet user interface) based on the selected facet-value pair, in order to produce a more focused set of results).
It would have been obvious at the effective filing date of the invention to a person having ordinary skill in the art to which said subject matter pertains to have modified Dredze with the teachings of Pattabiraman because it filters a large result set to display results that are most likely to matter to a job searcher on a personalized basis (Pattabiraman, Col. 4 Li. 13-14).
Kumar teaches the predictive value being computed based on a statistical correlation metric selected from chi-squared statistic or pointwise mutual information (Kumar, pa 0046, determine joint co-occurrence between the seed set keyword and another keyword using a normalization technique, such as point-wise mutual information… which quantifies how much more often two keywords co-occur in the predefined context than by random chance. If a keyword occurs with a high consistency with a seed set keyword, then it likely shares the discrimination properties with the seed set keyword and may be suggested as an alternate keyword.).
It would have been obvious at the effective filing date of the invention to a person having ordinary skill in the art to which said subject matter pertains to have modified Dredze in view of Pattabiraman with the teachings of Kumar because it assists in identifying keywords that may be useful to return further relevant documents in the set of documents (Kumar, pa 0044).
With respect to claim 2, Dredze in view of Pattabiraman and Kumar teaches the method according to claim 1, wherein the filter value further comprises a metadata tag (Dredze, Col. 8 Li. 16-27).
With respect to claim 3, Dredze in view of Pattabiraman and Kumar teaches the method according to claim 1, wherein the filter value is updated (Dredze, Col. 13 Li. 62-67, First, the Control Module 112 creates (626) a revised search query comprising the initial search query and the selected presentation facet. In some embodiments the revised search query is the concatenation of the text string of the initial query and a text string corresponding to the selected presentation facet).
With respect to claim 4, Dredze in view of Pattabiraman and Kumar teaches the method according to claim 1, wherein the received plurality of documents has been tagged with field values (Dredze, Col. 8 Li. 16-27, fields in documents).
With respect to claim 5, Dredze in view of Pattabiraman and Kumar teaches the method according to claim 1, further comprising: updating the filter value with the selection of the at least one of the filter criteria; and rebuilding the filter (Dredze, Col. 13 Li. 62-67, First, the Control Module 112 creates (626) a revised search query comprising the initial search query and the selected presentation facet. In some embodiments the revised search query is the concatenation of the text string of the initial query and a text string corresponding to the selected presentation facet).
With respect to claim 6, Dredze in view of Pattabiraman and Kumar teaches the method according to claim 5, further comprising using the rebuilt filter to identify additional documents found in a subsequent updated search (Dredze, Col. 14 Li. 11-14, After the revised set of search results is generated, Facet Generation Module 118 generates (608) a new set of candidate facets, and proceeds in the same way as processing the initial search query.).
With respect to claim 7, Dredze in view of Pattabiraman and Kumar teaches the method according to claim 1, further comprising generating the predictive value for each of the responsive phrases or predictive responsive concepts using any of chi- squared statistic or pointwise mutual information, the predictive value being indicative of how likely the responsive phrases or predictive responsive concepts are to be associated with documents of the received plurality of documents that are tagged with the filter value (Kumar, pa 0046, determine joint co-occurrence between the seed set keyword and another keyword using a normalization technique, such as point-wise mutual information… which quantifies how much more often two keywords co-occur in the predefined context than by random chance. If a keyword occurs with a high consistency with a seed set keyword, then it likely shares the discrimination properties with the seed set keyword and may be suggested as an alternate keyword.).
With respect to claim 8, Dredze in view of Pattabiraman and Kumar teaches the method according to claim 1, further comprising determining a frequency count value, the frequency count value indicating how many times the responsive phrases or predictive responsive concepts appear in the received plurality of documents (Dredze, Col. 16 Li. 35-38, Additional exemplary facet characteristics in the second category are based on simple counts of the candidate facets. In some embodiments, there is a facet characteristic based on the number of search results that match each candidate facet.).
With respect to claim 9, Dredze in view of Pattabiraman and Kumar teaches the method according to claim 1, further comprising determining the predictive value, the predictive value being indicative of how likely the responsive phrases or predictive responsive concepts are to be associated with documents of the received plurality of documents that are tagged with the filter value (Dredze, Fig. 6a, step 610 & Col. 9 Li. 45-52, Based on the facet list 224 (which includes the characteristic vectors), and the weights of the facet characteristics 228, Facet Ranking Module 120 ranks (516) the candidate facets to create a ranked list of candidate facets 518…the rank of each candidate facet is computed as the vector dot product of the weights w and the characteristic vector v, namely w·v.).
With respect to claims 10-20, the limitations are essentially the same as claims 1-9, and are rejected for the same reasons.
Response to Amendment
35 U.S.C. 112
With regard to claims 7-9, 11, and 18-20, the amendments to the claims have overcome the 35 U.S.C. 112 rejection. The Examiner withdraws the 35 U.S.C. 112 rejection to claims 7-9, 11, and 18-20.
Response to Arguments
35 U.S.C. 101
Applicant argues that the amendments directed towards filtering documents, based on a selected filter and filter criteria, amounts to a technical solution to integrate the exception into a practical application. The Examiner respectfully disagrees. Filtering documents has been found by the courts to be an abstract idea. The amendment adds a machine learning model, which is a generic computer component, and generating a graphical user interface comprising filter criteria. The claims disclose receiving a selection of filter criteria from the graphical user interface, however, the interface provides mere data gathering to obtain filter data that is used in the abstract mental process of generating. The claimed graphical user interface is recited at a high level of generality such that it only contains data to be output and selected by a user. Therefore, it recites insignificant extra-solution activity. The courts have found limitations directed to obtaining information electronically, recited at a high level of generality, to be well-understood, routine, and conventional. See MPEP 2106.05(d)(II), "electronic recordkeeping," and "storing and retrieving information in memory. The courts have found limitations directed towards outputting to be well-understood, routine, and conventional. See MPEP 2106.05(d)(II). Presenting offers and gathering statistics, OIP Techs., 788 F.3d at 1362-63, 115 USPQ2d at 1092-93.
Applicant argues that the claims are directed towards a specific improvement in the technology of filtering documents using a selected filter value and criteria by providing a technical effect achieved through a technical solution. The Examiner respectfully disagrees. Filtering documents by using a selected filter value is a process than can be done manually. The courts have stated that filtering content is an abstract idea because it is a longstanding, well-known method of organizing human behavior, similar to concepts previously found to be abstract. See Intellectual Ventures I LLC v. Capital One Bank (USA), 792 F.3d 1363, 1367 (Fed. Cir. 2015) (holding that "tracking financial transactions to determine whether they exceed a pre-set spending limit (i.e., budgeting)" is an abstract idea that "is not meaningfully different from the ideas found to be abstract in other cases ... involving methods of organizing human activity"); see also Content Extraction, 776 F.3d at 1347 (finding that "1) collecting data, 2) recognizing certain data within the collected data set, and 3) storing that recognized data in a memory" was an abstract idea because "data collection, recognition, and storage is undisputedly well-known" and "humans have always performed these functions"); Digitech Image Techs., LLC v. Elecs. for Imaging, Inc., 758 F.3d 1344, 1350 (Fed. Cir. 2014) (finding that "a process of organizing information through mathematical correlations" is an abstract idea). An abstract idea on "an Internet computer network" or on a generic computer is still an abstract idea. See Intellectual Ventures I, 792 F.3d at 1368 n. 2 (collecting cases). See BASCOM Global Internet v. AT&T Mobility, LLC, 827 F.3d 1341, 1345-46, 119 USPQ2d 1236, 1239 (Fed. Cir. 2016). The limitation of generating a predictive value using a machine learning model and based on statistical correlation or pointwise mutual information does nothing to provide a further improvement because it includes mental math and generic computer components. Even if the computation of the predictive value was too complex to be performed mentally, the computation would be abstract under the mathematical calculation grouping. Considering the additional elements individually and in combination and the claim as a whole, the additional elements do not provide significantly more than the abstract idea. The claim is not patent eligible.
35 U.S.C. 103
Applicant seems to argue a newly amended limitation. Applicant’s amendment has rendered the previous rejection moot. Upon further consideration of the amendment, a new grounds of rejection is made in view of Pattabiraman et al. (US 10,691,760).
Conclusion
Applicant's amendment necessitated the new ground(s) of rejection presented in this Office action. Accordingly, THIS ACTION IS MADE FINAL. See MPEP § 706.07(a). Applicant is reminded of the extension of time policy as set forth in 37 CFR 1.136(a).
A shortened statutory period for reply to this final action is set to expire THREE MONTHS from the mailing date of this action. In the event a first reply is filed within TWO MONTHS of the mailing date of this final action and the advisory action is not mailed until after the end of the THREE-MONTH shortened statutory period, then the shortened statutory period will expire on the date the advisory action is mailed, and any nonprovisional extension fee (37 CFR 1.17(a)) pursuant to 37 CFR 1.136(a) will be calculated from the mailing date of the advisory action. In no event, however, will the statutory period for reply expire later than SIX MONTHS from the mailing date of this final action.
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/BRITTANY N ALLEN/ Primary Examiner, Art Unit 2169