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 .
Claim Objections
Claim 11 is objected to because of the following informalities: the claim currently reads “user input associated the presented venue list” however it appears the claim should read “user input associated with the presented venue list”. 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 3, 7, 10, and 11 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 pre-AIA the applicant regards as the invention.
Re Claim 3 and 10, the limitation “the one or more venues” lacks antecedent basis for not being previously defined. It is unclear whether the limitation is referring to any venue or a candidate venue. For the purposes of examination the limitation will be interpreted as any venue.
Re Claim 7, the limitation “the set of venues” lacks antecedent basis for not being previous defined. For the purposes of examination the limitation will be interpreted as the candidate venues.
Re Claim 11, it is indeterminate as to which list the limitation “the venue list” is referencing as there is a venue list generated in the generating step and another venue list generated in the calibrating step in claim 1. For the purposes of examination the limitation will be interpreted as the calibrated venue list.
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.
Claim 1 is rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more.
Under Step 1 – the claim is directed toward a machine which is a statutory category.
Under Step 2A – Prong 1, the claim recites the limitations of generating a venue list using the sensor data, featurizing venue data associated with the one or more candidate venues to generate a feature set, generating metrics for the one or more candidate venues by applying the feature set to a probabilistic model, and calibrating the venue list based in part on the metrics. The generating, featurizing, and calibrating steps, as drafted, are simple processes that, under their broadest reasonable interpretation, covers performance of the limitation in the mind but for the recitation of by a processor. That is, other than reciting by a processor, nothing in the claim elements precludes the step from practically being performed in the mind. For example, but for the by a processor language, the claim compasses a person looking at data collected and forming a simple judgement. The mere nominal recitation of a processor does not take the claim limitations out of the mental process grouping. Thus, the claim recites a mental process.
Under Step 2A – Prong 2, the claim recites additional elements of a processor and receiving sensor data from a mobile device. The processor merely describes how to generally “apply” the otherwise mental judgements in a generic or general purpose computing environment. The processor is recited at a high level of generality and merely automates the method steps. The receiving step is recited at a high level of generality (i.e., as a general means of gathering data for use in the generating step), and amounts to mere data gathering, which is a form of insignificant extra-solution activity. Accordingly, even in combination, these additional elements do not integrate the abstract idea into a practical application because they do not impose any meaningful limits on practicing the abstract idea.
Under Step 2B – as discussed with respect to Step 2A – Prong 2, the additional elements in the claim amount to no more than insignificant extra-solution activity. Under the 2019 PEG, a conclusion that an additional element is insignificant extra-solution activity in Step 2A should be re-evaluated in Step 2B. Here, the receiving step was considered to be extra-solution activity in Step 2A, and thus it is re-evaluated in Step 2B to determine if it is more than what is well-understood, routine, conventional activity in the field. The background implies that the sensors are all conventional sensors mounted on a mobile device, and the specification does not provide any indication that the processor is anything other than a conventional computer processor. MPEP 2106.05(d)(II), and the cases cited therein, including Intellectual Ventures I, LLC v. Symantec Corp., 838 F.3d 1307, 1321 (Fed. Cir. 2016), TLI Communications LLC v. AV Auto. LLC, 823 F.3d 607, 610 (Fed. Cir. 2016), and OIP Techs., Inc., v. Amazon.com, Inc., 788 F.3d 1359, 1363 (Fed. Cir. 2015), indicate that mere collection or receipt of data over a network is a well‐understood, routine, and conventional function when it is claimed in a merely generic manner (as it is here). Accordingly, a conclusion that the receiving step is well-understood, routine, conventional activity is supported under Berkheimer. The claim is ineligible.
Claim 2 is also rejected for not further providing additional elements that provide significantly more than the judicial exception as it merely defines what the sensor data comprises.
Claim 3 is also rejected for not further providing additional elements that provide significantly more than the judicial exception as it merely defines what the sensor data is indicative of.
Claim 4 is also rejected for not further providing additional elements that provide significantly more than the judicial exception as it merely defines the featuring step as comprising of the steps of identifying one or more features in the venue data and using the one or more features to generate the feature set, which encompasses a person looking at data collected and forming a simple judgement and is thus a mental process.
Claim 5 is also rejected for not further providing additional elements that provide significantly more than the judicial exception as it merely defines what features are being identified/evaluated.
Claim 6 is also rejected for not further providing additional elements that provide significantly more than the judicial exception as it merely further defines the probabilistic model.
Claim 7 is also rejected for not further providing additional elements that provide significantly more than the judicial exception as it merely defines that the probabilistic model determines a confidence score for the one or more candidate venues, which encompasses a person looking at data collected and forming a simple judgement and is thus a mental process.
Claim 8 is also rejected for not further providing additional elements that provide significantly more than the judicial exception as it merely defines that the generating step comprises at least one of identifying venue information within the sensor data, which encompasses a person looking at data collected and forming a simple judgement and is thus a mental process, and providing a set of geographical coordinates representative of the location to a venue determination utility, which encompasses a first person orally communicating coordinates to a second person and is thus certain methods of organizing human activity.
Claim 9 is also rejected for not further providing additional elements that provide significantly more than the judicial exception as it merely further provides additional mental processes of accessing, providing, and using data which encompasses a person looking at data collected and forming a simple judgement.
Claim 10 is also rejected for not further providing additional elements that calibrating step comprises selecting a top ‘N’ venues, which encompasses a person looking at data collected and forming a simple judgement and is thus a mental process.
Claim 12 is also rejected for not further providing additional elements that provide significantly more than the judicial exception as it merely defines when the sensor data is received.
Claim 13 is rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more.
Under Step 1 – the claim is directed toward a process which is a statutory category.
Under Step 2A – Prong 1, the claim recites the limitations of generating a venue list using the sensor data, featurizing venue data associated with the one or more candidate venues to generate a feature set, generating metrics for the one or more candidate venues by applying the feature set to a probabilistic model, and calibrating the venue list based in part on the metrics. The generating, featurizing, and calibrating, , as drafted, are simple processes that, under their broadest reasonable interpretation, covers performance of the limitation in the mind but for the recitation of by a processor. That is, other than reciting by a processor, nothing in the claim elements precludes the step from practically being performed in the mind. For example, but for the by a processor language, the claim compasses a person looking at data collected and forming a simple judgement. The mere nominal recitation of a processor does not take the claim limitations out of the mental process grouping. Thus, the claim recites a mental process.
Under Step 2A – Prong 2, the claim recites additional elements of a receiving sensor data from a mobile device and presenting the calibrated venue list via a user interface. The receiving step is recited at a high level of generality (i.e., as a general means of gathering data for use in the generating step), and amounts to mere data gathering, which is a form of insignificant extra-solution activity. The presenting results via a user interface is also recited at a high level of generality (i.e., as a general means of displaying the calibrated venue list from the calibration step), and amounts to mere post solution displaying, which is a form of insignificant extra-solution activity. Accordingly, even in combination, these additional elements do not integrate the abstract idea into a practical application because they do not impose any meaningful limits on practicing the abstract idea.
Under Step 2B – as discussed with respect to Step 2A – Prong 2, the additional elements in the claim amount to no more than insignificant extra-solution activity. Under the 2019 PEG, a conclusion that an additional element is insignificant extra-solution activity in Step 2A should be re-evaluated in Step 2B. Here, the receiving step was considered to be extra-solution activity in Step 2A, and thus it is re-evaluated in Step 2B to determine if it is more than what is well-understood, routine, conventional activity in the field. The background implies that the sensors are all conventional sensors mounted on a mobile device, and the specification does not provide any indication that the processor is anything other than a conventional computer processor. MPEP 2106.05(d)(II), and the cases cited therein, including Intellectual Ventures I, LLC v. Symantec Corp., 838 F.3d 1307, 1321 (Fed. Cir. 2016), TLI Communications LLC v. AV Auto. LLC, 823 F.3d 607, 610 (Fed. Cir. 2016), and OIP Techs., Inc., v. Amazon.com, Inc., 788 F.3d 1359, 1363 (Fed. Cir. 2015), indicate that mere collection or receipt of data over a network is a well‐understood, routine, and conventional function when it is claimed in a merely generic manner (as it is here). Further, the the Federal Circuit in Trading Techs. Int’l v. IBG LLC, 921 F.3d 1084, 1093 (Fed. Cir. 2019), and Intellectual Ventures I LLC v. Erie Indemnity Co., 850 F.3d 1315, 1331 (Fed. Cir. 2017), for example, indicated that the mere displaying of data is a well understood, routine, and conventional function. Accordingly, a conclusion that the receiving step is well-understood, routine, conventional activity is supported under Berkheimer. The claim is ineligible.
Claim 14 is also rejected for not further providing additional elements that provide significantly more than the judicial exception as it merely further defines the probabilistic model.
Claim 15 is also rejected for not further providing additional elements that provide significantly more than the judicial exception as it merely further defines the parameters that are being evaluated.
Claim 16 is also rejected for not further providing additional elements that provide significantly more than the judicial exception as it merely defines what the sensor data is indicative of.
Claim 17 is also rejected for not further providing additional elements that provide significantly more than the judicial exception as it merely provides additional mental processes of accessing, providing, and scoring, which encompasses a person looking at data collected and forming a simple judgement and is thus a mental process.
Claim 18 is also rejected for not providing additional elements that provide significantly more than the judicial exception as it merely defines what the confidence metrics indicate.
Claim 19 is also rejected for not providing additional elements that provide significantly more than the judicial exception as it merely further defines the generating step as comprising determining a first set of venues in a first proximity to the location of the mobile device and further defines the calibrating steps as determining a second set of venues in a second proximity to the location of the mobile device, which encompasses a person looking at data collected and forming a simple judgement and is thus a mental process.
Claim 20 is rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more.
Under Step 1 – the claim is directed toward an article of manufacture which is a statutory category.
Under Step 2A – Prong 1, the claim recites the limitations of generating a venue list using the sensor data, featurizing venue data associated with the one or more candidate venues to generate a feature set, generating metrics for the one or more candidate venues by applying the feature set to a probabilistic model, and calibrating the venue list based in part on the metrics. The generating, featurizing, and calibrating steps, as drafted, are simple processes that, under their broadest reasonable interpretation, covers performance of the limitation in the mind but for the recitation of by a processor. That is, other than reciting by a processor, nothing in the claim elements precludes the step from practically being performed in the mind. For example, but for the by a processor language, the claim compasses a person looking at data collected and forming a simple judgement. The mere nominal recitation of a processor does not take the claim limitations out of the mental process grouping. Thus, the claim recites a mental process.
Under Step 2A – Prong 2, the claim recites additional elements of a processor, receiving sensor data from a mobile device, and presenting the calibrated venue list via a user interface. The processor merely describes how to generally “apply” the otherwise mental judgements in a generic or general purpose computing environment. The processor is recited at a high level of generality and merely automates the method steps. The receiving step is recited at a high level of generality (i.e., as a general means of gathering data for use in the generating step), and amounts to mere data gathering, which is a form of insignificant extra-solution activity. The presenting results via a user interface is also recited at a high level of generality (i.e., as a general means of displaying the calibrated venue list from the calibration step), and amounts to mere post solution displaying, which is a form of insignificant extra-solution activity. Accordingly, even in combination, these additional elements do not integrate the abstract idea into a practical application because they do not impose any meaningful limits on practicing the abstract idea.
Under Step 2B – as discussed with respect to Step 2A – Prong 2, the additional elements in the claim amount to no more than insignificant extra-solution activity. Under the 2019 PEG, a conclusion that an additional element is insignificant extra-solution activity in Step 2A should be re-evaluated in Step 2B. Here, the receiving step was considered to be extra-solution activity in Step 2A, and thus it is re-evaluated in Step 2B to determine if it is more than what is well-understood, routine, conventional activity in the field. The background implies that the sensors are all conventional sensors mounted on a mobile device, and the specification does not provide any indication that the processor is anything other than a conventional computer processor. MPEP 2106.05(d)(II), and the cases cited therein, including Intellectual Ventures I, LLC v. Symantec Corp., 838 F.3d 1307, 1321 (Fed. Cir. 2016), TLI Communications LLC v. AV Auto. LLC, 823 F.3d 607, 610 (Fed. Cir. 2016), and OIP Techs., Inc., v. Amazon.com, Inc., 788 F.3d 1359, 1363 (Fed. Cir. 2015), indicate that mere collection or receipt of data over a network is a well‐understood, routine, and conventional function when it is claimed in a merely generic manner (as it is here). Further, the the Federal Circuit in Trading Techs. Int’l v. IBG LLC, 921 F.3d 1084, 1093 (Fed. Cir. 2019), and Intellectual Ventures I LLC v. Erie Indemnity Co., 850 F.3d 1315, 1331 (Fed. Cir. 2017), for example, indicated that the mere displaying of data is a well understood, routine, and conventional function. Accordingly, a conclusion that the receiving step is well-understood, routine, conventional activity is supported under Berkheimer. The claim is ineligible.
Additionally, claim 20 is rejected under 35 U.S.C. 101 because the claimed invention is directed to non-statutory subject matter. The claim(s) does/do not fall within at least one of the four categories of patent eligible subject matter because it is interpreted as encompassing a signal per se.
Claim Rejections - 35 USC § 102
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 the appropriate paragraphs of 35 U.S.C. 102 that form the basis for the rejections under this section made in this Office action:
A person shall be entitled to a patent unless –
(a)(1) the claimed invention was patented, described in a printed publication, or in public use, on sale, or otherwise available to the public before the effective filing date of the claimed invention.
Claim(s) 1-8, 10, 12-16, and 18-20 is/are rejected under 35 U.S.C. 102(a)(1) as being anticipated by Kapicioglu (US 2013/0325855 A1).
As per claim 1, Kapicioglu discloses a system (data-processing system 210) comprising:
one or more processors (processor 301); and
memory coupled to at least one of the one or more processors, the memory comprising computer executable instructions that, when executed by the at least one processor, performs a method for venue detection (memory 302), the method comprising:
receiving sensor data from a mobile device, wherein the sensor data is associated with a location of the mobile device (see at least para. 90-96 for receiving spatial-temporal data of a user such as geolocation of wireless terminal 222 in task 605; see at least para. 47 for wireless terminal 222 is GPS-enabled);
generating a venue list using the sensor data, wherein the venue list comprises one or more candidate venues corresponding to the location of the mobile device (see at least para. 99 for generating set of candidate venues in relation to the geolocation of the user at task 610);
featurizing venue data associated with the one or more candidate venues to generate a feature set (see at least para. 107-108 for ranking candidate venues by applying hypothesis learned at task 515 and hypothesis incorporates whether venue is a visited venue vs. a non-visited venue, also accounts for popularity of venue, or history of having visited venue; the system would need to extract these features from the venues or have them already extracted before they can be used in an analysis);
generating metrics for the one or more candidate venues by applying the feature set to a probabilistic model (see at least para. 107-108 for ranking candidate venues by applying hypothesis learned at task 515 and hypothesis incorporates whether venue is a visited venue vs. a non-visited venue, also accounts for popularity of venue, or history of having visited venue; the hypothesis is equivalent to a probabilistic model as it calculates the likelihood the venue is the venue actually being checked-into, see at least Fig. 5); and
calibrating the venue list based in part on the metrics (see at least para. 107 for generating ranked set of candidate venues at task 615).
As per claim 2, Kapicioglu further discloses wherein the sensor data comprises at least one of:
geolocation data, positional data, Wi-Fi information, software information, hardware information, accelerometer data and time information (see at least para. 90-96 for receiving spatial-temporal data of a user such as geolocation of wireless terminal 222 in task 605).
As per claim 3, Kapicioglu further discloses wherein the sensor data is indicative of a visit by the mobile device to one or more venues (see at least para.4-5 and abstract for determining venue user is checking-into).
As per claim 4, Kapicioglu further discloses wherein featurizing the venue data comprises:
identifying one or more features in the venue data (see at least para. 107-108 for ranking candidate venues by applying hypothesis learned at task 515 and hypothesis incorporates whether venue is a visited venue vs. a non-visited venue, also accounts for popularity of venue, or history of having visited venue; the system would need to extract these features from the venues before they can be used in an analysis); and
using the one or more features to generate the feature set (see at least para. 107 for ranking candidate venues by applying hypothesis learned at task 515 and hypothesis incorporates whether venue is a visited venue vs. a non-visited venue, also accounts for popularity of venue, or history of having visited venue).
As per claim 5, Kapicioglu further discloses wherein identifying one or more features comprises evaluating at least one of: venue age, venue popularity, proximity to other venues, historical accuracy of venue candidates and previous venue visit data (see at least para. 107-108 for ranking candidate venues by applying hypothesis learned at task 515 and hypothesis incorporates whether venue is a visited venue vs. a non-visited venue, also accounts for popularity of venue, or history of having visited venue).
As per claim 6, Kapicioglu further discloses wherein the probabilistic model is an ensemble of decision trees generated using one or more gradient boosting techniques (see para. 79 for kd-tree).
As per claim 7, Kapicioglu further discloses wherein the probabilistic model determines a confidence score for the one or more candidate venues, wherein the confidence score indicates a probability a corresponding venue corresponds to the location of the mobile device (see example display of ranked candidate venues on Fig. 9; while a numerical value of a confidence score is not displayed, it is implied the venues at the top have a higher confidence score as the venues may be displayed in ranked order of most likely venue, see at least para. 120).
As per claim 8, Kapicioglu further discloses wherein generating the venue list comprises at least one of: identifying venue information within the sensor data, and providing a set of geographical coordinates representative of the location to a venue determination utility (see at least para. 90-96 for receiving spatial-temporal data, at data-processing system 210, of a user such as geolocation of wireless terminal 222 in task 605; see at least para. 47 for wireless terminal 222 is GPS-enabled).
As per claim 10, Kapicioglu further discloses wherein calibrating the venue list comprises selecting a top 'N' venues, wherein the top 'N' venues represent the one or more venues most likely to correspond to the location of a mobile device (see at least para. 120 for presenting top N candidate venues in terms of ranking).
As per claim 12, Kapicioglu further discloses wherein the sensor data is received in response to detecting at least one of a stop event by the mobile device and a visit event by the mobile device (see at least para. 4-5 and abstract for determining venue user is checking-into).
As per claim 13, Kapicioglu discloses a method for venue detection, the method comprising:
receiving sensor data from a mobile device, wherein the sensor data is associated with a location of the mobile device (see at least para. 90-96 for receiving spatial-temporal data of a user such as geolocation of wireless terminal 222 in task 605; see at least para. 47 for wireless terminal 222 is GPS-enabled);
generating a venue list using the sensor data, wherein the venue list comprises at least a first venue and a second venue, wherein the first venue and the second venue are associated with the location of the mobile device (see at least para. 99 for generating set of candidate venues in relation to the geolocation of the user at task 610);
featurizing venue data associated with the first venue and the second venue to generate a feature set (see at least para. 107 for ranking candidate venues by applying hypothesis learned at task 515 and hypothesis incorporates whether venue is a visited venue vs. a non-visited venue, also accounts for popularity of venue, or history of having visited venue; the system would need to extract these features from the venues or have them already extracted before they can be used in an analysis);
generating confidence metrics for the first venue and the second venue by applying the feature set to a probabilistic model (see at least para. 107 for ranking candidate venues by applying hypothesis learned at task 515 and hypothesis incorporates whether venue is a visited venue vs. a non-visited venue, also accounts for popularity of venue, or history of having visited venue; the hypothesis is equivalent to a probabilistic model as it calculates the likelihood the venue is a the venue actually being checked-into, see at least Fig. 5; while a numerical value of a confidence score is not explicitly generated, it is implied the venues ranked at the top have a higher confidence score as the venues may be displayed in ranked order of most likely venue, see at least para. 120);
calibrating the venue list based in part on the metrics (see at least para. 107 for generating ranked set of candidate venues at task 615); and
presenting the calibrated venue list and at least a portion of the confidence metrics via a user interface, wherein at least one of the first venue and the second venue corresponds to the location of the mobile device (see example display of ranked candidate venues on Fig. 9; while a numerical value of a confidence score is not displayed, it is implied the venues at the top have a higher confidence score as the venues may be displayed in ranked order of most likely venue, see at least para. 120).
As per claim 14, Kapicioglu further discloses wherein the probabilistic model is an ensemble of decision trees generated using one or more gradient boosting techniques (see para. 79 for kd-tree).
As per claim 15, Kapicoglu further discloses wherein the one or more gradient boosting techniques evaluate at least one of: venue age, venue popularity, proximity to other venues, historical accuracy of venue candidates, previous venue visit data, and user feedback (see at least para. 107-108 for ranking candidate venues by applying hypothesis learned at task 515 and hypothesis incorporates whether venue is a visited venue vs. a non-visited venue, also accounts for popularity of venue, or history of having visited venue).
As per claim 16, Kapicioglu further discloses wherein the sensor data is indicative of a visit by the mobile device to at least one of the first venue and the second venue (see at least para. 4-5 and abstract for determining venue user is checking-into).
As per claim 18, Kapicioglu further discloses wherein the confidence metrics indicate a probability a respective venue corresponds to the location of the mobile device (see at least para. 107 for ranking candidate venues by applying hypothesis learned at task 515 and hypothesis incorporates whether venue is a visited venue vs. a non-visited venue, also accounts for popularity of venue, or history of having visited venue; the hypothesis is equivalent to a probabilistic model as it calculates the likelihood the venue is a the venue actually being checked-into, see at least Fig. 5; while a numerical value of a confidence score is not explicitly generated, it is implied the venues ranked at the top have a higher confidence score as the venues may be displayed in ranked order of most likely venue, see at least para. 120).
As per claim 19, Kapicioglu further discloses wherein the generating the venue list comprises determining a first set of venues in a first proximity to the location of the mobile device, and wherein calibrating the venue list comprises determining a second set of venues in a second proximity to the location of the mobile device, wherein the second set of venues comprises fewer venues than the first set of venues (see at least para. 100 for second ranking based on distance to limit geographic area under consideration).
As per claim 20, Kapicioglu discloses a computer-readable storage medium encoding computer executable instructions which, when executed by at least one processor, performs a method for venue detection (see at least para. 124 for computer-readable medium), the method comprising:
receiving sensor data from a mobile device, wherein the sensor data is associated with a location of the mobile device (see at least para. 90-96 for receiving spatial-temporal data of a user such as geolocation of wireless terminal 222 in task 605; see at least para. 47 for wireless terminal 222 is GPS-enabled);
generating a venue list using the sensor data, wherein the venue list comprises at least a first venue and a second venue, wherein the first venue and the second venue are associated with the location of the mobile device (see at least para. 99 for generating set of candidate venues in relation to the geolocation of the user at task 610);
featurizing venue data associated with the first venue and the second venue to generate a feature set (see at least para. 107 for ranking candidate venues by applying hypothesis learned at task 515 and hypothesis incorporates whether venue is a visited venue vs. a non-visited venue, also accounts for popularity of venue, or history of having visited venue; the system would need to extract these features from the venues or have them already extracted before they can be used in an analysis);
generating confidence metrics for the first venue and the second venue by applying the feature set to a probabilistic model (see at least para. 107 for ranking candidate venues by applying hypothesis learned at task 515 and hypothesis incorporates whether venue is a visited venue vs. a non-visited venue, also accounts for popularity of venue, or history of having visited venue; the hypothesis is equivalent to a probabilistic model as it calculates the likelihood the venue is a the venue actually being checked-into, see at least Fig. 5; while a numerical value of a confidence score is not explicitly generated, it is implied the venues ranked at the top have a higher confidence score as the venues may be displayed in ranked order of most likely venue, see at least para. 120);
calibrating the venue list based in part on the metrics (see at least para. 107 for generating ranked set of candidate venues at task 615); and
presenting the calibrated venue list and at least a portion of the confidence metrics via a user interface, wherein at least one of the first venue and the second venue corresponds to the location of the mobile device (see example display of ranked candidate venues on Fig. 9; while a numerical value of a confidence score is not displayed, it is implied the venues at the top have a higher confidence score as the venues may be displayed in ranked order of most likely venue, see at least para. 120).
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.
Claims 9 and 17 is/are rejected under 35 U.S.C. 103 as being unpatentable over Kapicioglu (US 2013/0325855 A1) in view of Lin (US 2012/0284212 A1).
As per claim 9, Kapicioglu is silent regarding, but Lin teaches:
accessing a set of training data comprising previously received input data and corresponding output data (see at least para. 3 for training data);
providing output data generated by the probabilistic model to an objective function and using the objective function to compare the output data to training data comprising previously received input data and corresponding result data (see at least para. 3 for comparing input/output data of a predictive model to input/output data of a training data set); and
wherein the objective function defines one or more parameters for evaluation (see at least para. 3 for determining an accuracy score based on comparison of predictive model to training data).
It would have been obvious to one of ordinary skill in the art before the effectively filed date of the invention to modify the system of Kapicioglu with the features taught by Lin for determining an accuracy score for a predictive model compared to known training data in order to best predict an output such predicting future trends, behavior patterns, or performing sentiment analysis.
As per claim 17, Kapicioglu is silent regarding, but Lin teaches:
accessing a set of training data comprising previously received input data and corresponding output data (see at least para. 3 for training data);
providing output data generated by the probabilistic model to an objective function and using the objective function to compare the output data to training data comprising previously received input data and corresponding result data (see at least para. 3 for comparing input/output data of a predictive model to input/output data of a training data set); and
based on the comparison, scoring the effectiveness of the probabilistic model (see at least para. 3 for determining an accuracy score based on comparison of predictive model to training data).
It would have been obvious to one of ordinary skill in the art before the effectively filed date of the invention to modify the method of Kapicioglu with the features taught by Lin for using known training data to determine an accuracy score for a predictive model.
Claim 11 is/are rejected under 35 U.S.C. 103 as being unpatentable over Kapicioglu (US 2013/0325855 A1) in view of Moore (US 2014/0358836 A1).
As per claim 11, Kapicioglu teaches wherein the method further comprises:
presenting the calibrated venue list using a user interface (see example display of ranked candidate venues on Fig. 9);
Kapicioglu is silent regarding, but Moore et al teaches:
receiving, via the user interface, user input associated the calibrated venue list (see at least para. 20 and claim 31 for user selectively filtering venue locations within display); and
using the user input to modify the calibrated venue list (see at least para. 20 and claim 31 for user selectively filtering venue locations within display).
It would have been obvious to one of ordinary skill in the art before the effectively filed date of the invention to modify the method of Kapicioglu with the features taught by Moore for allowing a user to interact with the displayed venue list to selectively filter the venue locations on the display.
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 USPTO Internet website contains terminal disclaimer forms which may be used. Please visit www.uspto.gov/patent/patents-forms. The 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/process/file/efs/guidance/eTD-info-I.jsp.
Claims 1-16 and 18-20 are rejected on the ground of nonstatutory double patenting as being unpatentable over claims 1-6 and 11-20 of U.S. Patent No. US 11,481,690. Although the claims at issue are not identical, they are not patentably distinct from each other because the instant claims are broader in scope than the patent claims such that the patent claims infringe upon the instant claims.
Instant claim
Corresponding patent claim
1
11
2
12
3
13
4
14
5
15
6
16
7
17
8
18
9, 11
11
10
19
12
20
13, 14
1
15
2
16
3
18
5
19
6
20
1
Re claim 20, it would have been obvious to one of ordinary skill in the art to create a computer-readable storage medium containing instructions to perform the method of patent claim 1 because the method would be able to automated by executing the instructions by a processor.
Claim 17 is rejected on the ground of nonstatutory double patenting as being unpatentable over claim 4 of U.S. Patent No. US 11,481,690 in view of Lin (US 2012/0284212 A1).
Patent claim 4 discloses the limitation except for accessing a set of training data comprising previously received input data and corresponding output data.
However, Lin teaches accessing a set of training data comprising previously received input data and corresponding output data (see at least [0003] for training data).
It would have been obvious to one of ordinary skill in the art before the effectively filed date of the invention to modify the method of patent claim 4 with the features taught by Lin for using known training data to determine an accuracy score for a predictive model.
Claims 1-3 and 5-20 are rejected on the ground of nonstatutory double patenting as being unpatentable over claims 1-19 of U.S. Patent No. US 12,086,699. Although the claims at issue are not identical, they are not patentably distinct from each other because the instant claims are broader in scope than the patent claims such that the patent claims infringe upon the instant claims.
Instant claim
Corresponding patent claim
1
9
2
10
3
11
5
12
6
13
7
14
8
15
9
16
10
17
11
18
12
19
13
1
14
2
15
3
16
4
17
5
18
6
19
7
20
8
Claim 4 is rejected on the ground of nonstatutory double patenting as being unpatentable over claim 9 of U.S. Patent No. US 12,086,699 in view of Kapicioglu (US 2013/0325855 A1).
Patent claim 4 discloses the limitation except for wherein featurizing the venue data comprises: identifying one or more features in the venue data; and using the one or more features to generate the feature set.
However, Kapicioglu further discloses wherein featurizing the venue data comprises:
identifying one or more features in the venue data (see at least para. 107-108 for ranking candidate venues by applying hypothesis learned at task 515 and hypothesis incorporates whether venue is a visited venue vs. a non-visited venue, also accounts for popularity of venue, or history of having visited venue; the system would need to extract these features from the venues before they can be used in an analysis); and
using the one or more features to generate the feature set (see at least para. 107 for ranking candidate venues by applying hypothesis learned at task 515 and hypothesis incorporates whether venue is a visited venue vs. a non-visited venue, also accounts for popularity of venue, or history of having visited venue).
It would have been obvious to one of ordinary skill in the art before the effectively filed date of the invention to modify the method of patent claim 4 with the features taught by Kapicioglu for determining venue specific metrics to rank candidate venues in order to determine the most likely venue.
Conclusion
Any inquiry concerning this communication or earlier communications from the examiner should be directed to ROBERT NGUYEN whose telephone number is (571)272-4838. The examiner can normally be reached M-F 8AM - 4PM ET.
Examiner interviews are available via telephone, in-person, and video conferencing using a USPTO supplied web-based collaboration tool. To schedule an interview, applicant is encouraged to use the USPTO Automated Interview Request (AIR) at http://www.uspto.gov/interviewpractice.
If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, ANITA COUPE can be reached at (571) 270-3614. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
Information regarding the status of published or unpublished applications may be obtained from Patent Center. Unpublished application information in Patent Center is available to registered users. To file and manage patent submissions in Patent Center, visit: https://patentcenter.uspto.gov. Visit https://www.uspto.gov/patents/apply/patent-center for more information about Patent Center and https://www.uspto.gov/patents/docx for information about filing in DOCX format. For additional questions, contact the Electronic Business Center (EBC) at 866-217-9197 (toll-free). If you would like assistance from a USPTO Customer Service Representative, call 800-786-9199 (IN USA OR CANADA) or 571-272-1000.
/ROBERT T NGUYEN/PRIMARY EXAMINER, Art Unit 3619