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 . The following is a Final Office Action. Claims 1-7, 10-16, and 19-23 are rejected below.
Response to Amendment
Applicant’s amendments are acknowledged.
Response to Arguments
Applicant’s arguments with respect to 101 Rejection have been fully considered but are non-persuasive.
Applicant argues that the recited features including the newly added amendments are not directed to an abstract idea. Applicant states the Sentence-BERT neural network that is dynamically updated based on the calibrating is incapable of being performed in the human mind and the recommender engine feature and Sentence-BERT neural network and recited in such great detail that it cannot be construed as a generic computer component.
Examiner responds a dynamic updating can still be performed in the human mind and each of the details are still part of the abstract idea. The Sentence-BERT neural network and the recommender engine feature are considered additional elements. The Sentence-BERT neural network and the recommender engine that utilizes an online machine learning model are generally linking the use of the abstract idea to a particular technological environment or field of use under MPEP 2106.05(h)
Applicant argues the amendments integrate the claims into a practical application.
Applicant argues the amendments including the dynamic updating of a Sentence-BERT neural network model based on the calibrating to adjust parameters are in such great detail that they cannot reasonably be considered to be claimed at a high level of generality.
Examiner responds the dynamic updating and the details can be performed in the human mind. The Sentence-BERT neural network is considered an additional element and generally linking the use of the abstract idea to a particular technological environment or field of use under MPEP 2106.05(h)
Applicant argues the newly-claimed details are inextricably tied to and solve problems in current computer technology and cites limitations such as retrieving and selecting relevant news information, predicting confidence scores, taking into account user feedback for the retrieving process, and continuously adjusting the confidence scores based on the user feedback, which significantly increases the speed and accuracy of identifying institutional risks based on news information.
Examiner responds the steps above can be performed in the human mind and are part of the abstract idea. In addition, these steps are solving business-related problems and are not exclusively linked to technology.
Applicant argues the amendments recite significantly more than an abstract idea because the claims recite a particular ordered combination of claimed element such that the combination includes an inventive concept and cites Bascom. Applicant states this is supported by withdrawal of the 103 Rejection and no prior art is applied.
Examiner responds In Bascom the claims illustrated an inventive concept that set forth a particular non-conventional arrangement of conventional elements that was essential to the implementation of the steps in a particular order. The inventive concept described and claimed in Bascom is the installation of a filtering tool at a specific location, remote from the end-users, with customizable filtering features specific to each end user. This design gives the filtering tool both the benefits of a filter on a local computer and the benefits of a filter on the ISP server.”
The instant application’s claims merely recite generic computer elements by which the steps are executed, there is not a non-conventional arrangement of the computer elements, but instead a generic computer merely applies the abstract idea.
As stated above, the dynamic updating and the referenced steps can be performed in the human mind. The online machine learning model and the Sentence-BERT neural network are recited with high generality and generally link the use of the abstract idea to a particular technological environment or field of use under MPEP 2106.05(h).
See Also 0095, Spec - The use of “machine learning” is described in the Spec in such as manner as to indicate that the additional element is sufficiently well-known in the art .
A “Sentence-BERT” neural network is described in Yang, 0031 in such as manner as to indicate that the additional element is sufficiently well-known in the art [0031] While existing MLMs like BERT include next sentence prediction tasks...)
The limitation ” integrating, by the at least one processor, the Sentence-BERT neural network into at least one from among a risk trigger embeddings database and a news embeddings database in order to determine respective cosine similarity rankings for providing recommendations regarding news relevance” – under BRI is interpreted as storing the neural network in a database which is considered insignificant extrasolution data storing activities. See MPEP 2106.05(d) Well-Understood, Routine, Conventional Activity [R-08.2017]
II. ELEMENTS THAT THE COURTS HAVE RECOGNIZED AS WELL-UNDERSTOOD, ROUTINE, CONVENTIONAL ACTIVITY IN PARTICULAR FIELDS
iii. Electronic recordkeeping, Alice Corp., 134 S. Ct. at 2359, 110 USPQ2d at 1984 (creating and maintaining “shadow accounts”); Ultramercial, 772 F.3d at 716, 112 USPQ2d at 1755 (updating an activity log);
iv. Storing and retrieving information in memory, Versata Dev. Group, Inc. v. SAP Am., Inc., 793 F.3d 1306, 1334, 115 USPQ2d 1681, 1701 (Fed. Cir. 2015); OIP Techs., 788 F.3d at 1363, 115 USPQ2d at 1092-93;
The prior art section can have no bearing on the 101 Section.
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 1-7, 10-16, 19-23 are rejected under 35 U.S.C. 101 because the claimed invention is directed to a judicial exception (i.e., a law of nature, a natural phenomenon, or an abstract idea) without significantly more.
Claims 1-7, 10-16, 19-23 are rejected under 35 U.S.C. 101 because the claimed invention is directed to non-statutory subject matter. Specifically Claims 1-7, 10-16, 19-23 are directed to an abstract idea without additional elements amounting to significantly more than the abstract idea.
Step 1 of the Alice/Mayo analysis is directed to determining whether or not the claims fall within a statutory class. Based on a facial reading of the claim elements, Claims 1-7, 10-16, 19-23 fall within a statutory class of process, machine, manufacture, or composition of matter.
With respect to Step 2A Prong One of the framework, the claims recite an abstract idea. Claims 1, 10, and 19 include limitations reciting identifying institutional risk based on news information, including steps:
Receiving textual information...
Analyzing the received textual information
Retrieving at least one news item based on a result of the analyzing and a first confidence score...
Generating a vector representation of each corresponding headline
...Comparing the received textual information with the retrieved at least one new item
Calibrating...the first confidence score...
Wherein the calibrating includes
Predicting...whether each corresponding headline is relevant to a risk item...
Assigning a first confidence score...
Receiving an input regarding whether each corresponding headline is relevant to the risk item;
Continuously modifying the first confidence score...
Wherein the model is dynamically updated based on calibrating to adjust parameters associated with the obtaining of the metric that relates to the degree of relevance of the retrieved at least one news item to the potential risk
which is an abstract idea reasonably categorized as
Mental processes (ie. gathering data and processing it with algorithmic steps in order to make evaluations/judgments). Examiner notes each of the steps can be performed in the human mind.
Claim 2-7, 11-16, and 20-23 further describe making determinations and descriptive data that further narrow the abstract idea.
Examiner notes in Claims 21-23 – the “interactive feedback mechanism”; a user tagging a headline, a model being dynamically updated by continually updating each confidence score are steps that can be performed in the human mind.
With respect to Step 2A Prong Two, the claims do not include additional elements that integrate the abstract idea into a practical application. Claims 1, 10, and 19 include various elements that are not directed to the abstract idea under Step 2A Prong One of the framework. These additional elements include processor, memory, communication interface, computer readable storage medium, executable code, risk trigger embeddings database, news embeddings database. When considered in view of the claim as a whole, Examiner submits that the additional elements do not integrate the abstract idea into a practical application because these elements are generic computing elements performing generic computing functions and amount to mere instructions to apply the abstract idea on a computer under MPEP 2106.05(f).
The use of the Sentence-BERT neural network generally links the use of the abstract idea to a particular technological environment or field of use under MPEP 2106.05(h). The functions of the Sentence-BERT neural network- comparing data to obtaining a metric - are input/outputs that do not provide an improvement to machine learning technology.
The use of the online machine learning model by the recommender engine generally links the use of the abstract idea to a particular technological environment or field of use under MPEP 2106.05(h). The functions recited such as predicting, assigning, receiving, and modifying are basic functions and do not recite improvements to machine learning technology.
Under BRI - ” integrating, by the at least one processor, the Sentence-BERT neural network into at least one from among a risk trigger embeddings database and a news embeddings database in order to determine respective cosine similarity rankings for providing recommendations regarding news relevance” – under BRI is interpreted as storing the neural network in a database which is considered insignificant extrasolution data storing activities.
Claim 2, 4-6, 11, 13-15, 19, and 23 do not include additional elements above and beyond claims 1, 10, and 19.
In Claims 3 and 12, the use of the bi-LATM neural network sequence prediction model also generally links the use of the abstract idea to a particular technological environment or field of use under MPEP 2106.05(h).
As a result, Claims 1-7, 11-16, and 19-23 do not include additional elements that would integrate the abstract idea into a practical application under Step 2A Prong Two.
With respect to Step 2B of the framework, the claims do not include additional elements amounting to significantly more than the abstract idea. Claims 1, 10, and 19 includes various elements that are not directed to the abstract idea under Step 2A Prong One of the framework. These additional elements include processor, memory, communication interface, computer readable storage medium, executable code, risk trigger embeddings database, news embeddings database. When considered in view of the claim as a whole, Examiner submits that the additional elements do not amount to significantly more than the abstract idea because these elements are generic computing elements performing generic computing functions and amount to mere instructions to apply the abstract idea on a computer under MPEP 2106.05(f) and/or recite generic computer structure that serves to perform generic computer functions that are well-understood, routine, and conventional activities previously known to the pertinent industry.
The use of the recommender engine that utilizes an online machine learning model is recited with high generality and generally links the use of the abstract idea to a particular technological environment or field of use under MPEP 2106.05(h). The use of “machine learning” is described in the Spec in such as manner as to indicate that the additional element is sufficiently well-known in the art (0095) -“In traditional Machine Learning, a static set of training examples is provided, based on which a model is trained to estimate optimal parameters. After the training stage, the model is deployed with the optimal parameters, which are no longer adjusted...”
The use of the “Sentence-BERT” neural network is described in Yang, 0031 in such as manner as to indicate that the additional element is sufficiently well-known in the art [0031] While existing MLMs like BERT include next sentence prediction tasks...)
As stated above - ” integrating, by the at least one processor, the Sentence-BERT neural network into at least one from among a risk trigger embeddings database and a news embeddings database in order to determine respective cosine similarity rankings for providing recommendations regarding news relevance” – is interpreted as storing the neural network in a database which is considered insignificant extrasolution data storing activities. See MPEP 2106.05(d) Well-Understood, Routine, Conventional Activity [R-08.2017]
II. ELEMENTS THAT THE COURTS HAVE RECOGNIZED AS WELL-UNDERSTOOD, ROUTINE, CONVENTIONAL ACTIVITY IN PARTICULAR FIELDS
iii. Electronic recordkeeping, Alice Corp., 134 S. Ct. at 2359, 110 USPQ2d at 1984 (creating and maintaining “shadow accounts”); Ultramercial, 772 F.3d at 716, 112 USPQ2d at 1755 (updating an activity log);
iv. Storing and retrieving information in memory, Versata Dev. Group, Inc. v. SAP Am., Inc., 793 F.3d 1306, 1334, 115 USPQ2d 1681, 1701 (Fed. Cir. 2015); OIP Techs., 788 F.3d at 1363, 115 USPQ2d at 1092-93;
Claim 2, 4-6, 11, 13-15, and 20 do not include additional elements above and beyond claims 1, 10, and 19 and thus do not provide significantly more to the abstract idea.
Claims 3 and 12 includes the additional element a “bi-LSTM” neural network sequence prediction model.” However, this element is described in the Spec, 0082 in such a manner as to indicate that the additional element is sufficiently well-known in the art. [0082] Several approaches were tested to decompose the text into the three aforementioned categories. One of these is based on a deep bidirectional long short term memory (bi-LSTM) neural network sequence prediction model that was originally developed for supervised open information extraction.
Thus, Claims 1-7, 10-16, and 19-23 do not provide significantly more to the abstract idea.
Accordingly, Claims 1-7, 10-16, and 19-23 are rejected under 35 U.S.C. 101 as being directed to non-statutory subject matter.
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure. [AltContent: rect]
Applicant's amendment necessitated the new ground(s) of rejection presented in this Office action. Accordingly, THIS ACTION IS MADE FINAL. See MPEP § 706.07(a). Applicant is reminded of the extension of time policy as set forth in 37 CFR 1.136(a).
A shortened statutory period for reply to this final action is set to expire THREE MONTHS from the mailing date of this action. In the event a first reply is filed within TWO MONTHS of the mailing date of this final action and the advisory action is not mailed until after the end of the THREE-MONTH shortened statutory period, then the shortened statutory period will expire on the date the advisory action is mailed, and any extension fee pursuant to 37 CFR 1.136(a) will be calculated from the mailing date of the advisory action. In no event, however, will the statutory period for reply expire later than SIX MONTHS from the date of this final action.
Any inquiry concerning this communication or earlier communications from the examiner should be directed to SCOTT ROSS whose telephone number is (571) 270-1555. The examiner can normally be reached on Monday-Friday 8:00 AM - 5:00 PM E.S.T..
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If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, Rutao Wu, can be reached on (571) 272-6045. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
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/Scott Ross/
Examiner - Art Unit 3623
/RUTAO WU/Supervisory Patent Examiner, Art Unit 3623