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 .
Information Disclosure Statement
The information disclosure statements (IDS) submitted on 01/22/2024 were filed before the mailing date of the first office action. The submissions are in compliance with the provisions of 37 CFR 1.97. Accordingly, the information disclosure statements are being considered by the examiner.
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 1-3, 5-12, 14-17, and 19-20 of U.S. Patent No. 11,461,698. Although the claims at issue are not identical, they are not patentably distinct from each other because the claims are substantially similar to each other as discussed below.
Instant Application
Patent No. 11,461,698
1. A computer-implemented method for training an artificial intelligence (AI) engine for a topic, comprising:
1. A computer implemented method for training an artificial intelligence (Al) engine for a topic, comprising:
training, by at least one processor, the Al engine for the topic using data from a data source, wherein the topic is associated with a first geolocation;
training, by at least one processor, the Al engine for the topic using data from a data source, wherein the topic is associated with a first geolocation;
first receiving a first set of predictive features for the topic from the trained Al engine;
first receiving, by the at least one processor, a first set of predictive features for the topic from the trained Al engine;
transmitting the first set of predictive features for the topic to a set of electronic devices;
transmitting, by the at least one processor, the first set of predictive features for the topic to a set of electronic devices;
second receiving a first set of audiovisual content captured by the set of electronic devices based on the first set of predictive features for the topic;
second receiving, by the at least one processor, a first set of audiovisual content captured by the set of electronic devices based on the first set of predictive features for the topic;
determining, using the trained Al engine, that audiovisual content in the first set of audiovisual content is relevant to the topic based on the first set of predictive features and a second geolocation associated with the first set of audiovisual content;
and retraining the AI engine based on the first set of audiovisual content.
and retraining, by the at least one processor, the Al engine based on the first set of audiovisual content
Claim 1 of the instant application is anticipated by patent claim 1 in that claim 1 of the patent contains all the limitations of claim 1 of the instant application. Claim 1 of the instant application therefore is not patently distinct from the earlier patent claim and as such is unpatentable. Dependent claims 2-3 and 5-8 of the instant application are anticipated by patent claims 2-3 and 5-8, and the limitation in claim 4 of the instant application is anticipated by patent claim 1.
Similarly, claims 9-15 of the instant application are anticipated by patent claims 9-12 and 14-15, and claims 16-20 of the instant application are anticipated by parent claims 16-17 and 19-20.
Claims 1-20 are rejected on the ground of nonstatutory double patenting as being unpatentable over claims 1-5, 7-12, 14-18, and 20 of U.S. Patent No. 11,669,777. Although the claims at issue are not identical, they are not patentably distinct from each other.
Instant Application
Patent No. 11,669,777
1. A computer-implemented method for training an artificial intelligence (AI) engine for a topic, comprising:
A computer-implemented method for training an artificial intelligence (AI) engine for a topic, comprising:
training, by at least one processor, the Al engine for the topic using data from a data source, wherein the topic is associated with a first geolocation;
training, by at least one processor, the AI engine for the topic using data from a data source, wherein the topic is associated with a first geolocation;
first receiving a first set of predictive features for the topic from the trained Al engine;
first receiving a first set of predictive features for the topic from the trained AI engine;
transmitting the first set of predictive features for the topic to a set of electronic devices;
transmitting the first set of predictive features for the topic to a set of electronic devices;
second receiving a first set of audiovisual content captured by the set of electronic devices based on the first set of predictive features for the topic;
second receiving a first set of audiovisual content captured by the set of electronic devices based on the first set of predictive features for the topic;
triggering an intervention action based on a threshold number of predictive features of the first set of predictive features being present in audiovisual content in the first set of audiovisual content,
wherein the audiovisual content in the first set of audiovisual content is captured by an electronic device in the set of electronic devices;
notifying the electronic device in the set of electronic devices of the triggered intervention;
and retraining the AI engine based on the first set of audiovisual content.
and retraining the AI engine based on the first set of audiovisual content.
Claim 1 of the instant application is anticipated by patent claim 1 in that claim 1 of the patent contains all the limitations of claim 1 of the instant application. Claim 1 of the instant application therefore is not patently distinct from the earlier patent claim and as such is unpatentable. Dependent claims 2-5 and 7-8 of the instant application are anticipated by patent claims 2-5 and 7-8, and the limitation in claim 6 of the instant application is anticipated by patent claim 1.
Similarly, claims 9-15 of the instant application are anticipated by patent claims 9-12 and 14-15, and claims 16-20 of the instant application are anticipated by parent claims 16-18 and 20.
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 4, 8, 12, 15, and 18 are rejected under 35 U.S.C. 101. Claims 4 and 8 are directed to a method, claims 12 and 15 are directed to a system, and claim 18 is directed to a non-transitory computer-readable medium; therefore, claims 4, 8, 12, 15, and 18 fall within one of the four statutory categories (i.e., process, machine, manufacture, or composition of matter). However, claims 4, 8, 12, 15, and 18 fall within the judicial exception of an abstract idea, specifically the abstract ideas of “Mental Processes” (including observation, evaluation, and opinion) and “Mathematical Concepts (including mathematical calculations and relationships)”.
Claim 4:
Step 1: Claim 4 is directed to a method; therefore, the claim does fall within one of the four statutory categories (i.e., process, machine, manufacture, or composition of matter).
Step 2A, Prong 1: Claim 4 recites the following abstract ideas:
determining, using the trained AI engine, that audiovisual content in the first set of audiovisual content is relevant to the topic based on the first set of predictive features and a second geolocation associated with the first set of audiovisual content (mental step directed to observation, evaluation – a person could determine whether observed audiovisual content is relevant to a topic in their mind based on an observed or mentally determined set of predictive features and geolocation associated with the observed audiovisual content. As the claims do not recite any particular AI engine nor technical steps for training an AI engine, the AI engine is interpreted as a generic computer component used to merely apply the claimed abstract idea (see MPEP 2106.05(f)).
Step 2A, Prong 2: as claim 4 depends on claim 1, claim 4 recites the following additional elements from the limitations in claim 1:
training, by at least one processor, the Al engine for the topic using data from a data source, wherein the topic is associated with a first geolocation; first receiving a first set of predictive features for the topic from the trained Al engine; transmitting the first set of predictive features for the topic to a set of electronic devices; second receiving a first set of audiovisual content captured by the set of electronic devices based on the first set of predictive features for the topic; and retraining the AI engine based on the first set of audiovisual content.
As the claims do not recite any particular AI engine nor technical steps for training an AI engine, the AI engine is interpreted as a generic computer component and the training and retraining steps are interpreted as generic computer activity. Receiving a set of predictive features from a trained AI engine is interpreted as insignificant extra-solution activity directed to mere data gathering. Transmitting a set of predictive features to a set of electronic devices is interpreted as insignificant extra-solution activity directed to mere data gathering. Receiving a set of audiovisual content from a set of electronic devices is interpreted as insignificant extra-solution activity directed to mere data gathering. These additional elements do not integrate the abstract idea into a practical application.
Step 2B: as claim 4 depends on claim 1, claim 4 recites the following additional elements from the limitations in claim 1:
training, by at least one processor, the Al engine for the topic using data from a data source, wherein the topic is associated with a first geolocation; first receiving a first set of predictive features for the topic from the trained Al engine; transmitting the first set of predictive features for the topic to a set of electronic devices; second receiving a first set of audiovisual content captured by the set of electronic devices based on the first set of predictive features for the topic; and retraining the AI engine based on the first set of audiovisual content.
As the claims do not recite any particular AI engine nor technical steps for training an AI engine, the AI engine is interpreted as a generic computer component and the training and retraining steps are interpreted as generic computer activity. Receiving a set of predictive features from a trained AI engine is interpreted as well-understood, routine, conventional activity directed to transmitting and receiving data over a network. Transmitting a set of predictive features to a set of electronic devices is interpreted as well-understood, routine, conventional activity directed to transmitting and receiving data over a network. Receiving a set of audiovisual content from a set of electronic devices is interpreted as well-understood, routine, conventional activity directed to transmitting and receiving data over a network. These additional elements do not amount to significantly more (see MPEP 2106.05(d)(II) and MPEP 2106.05(g)).
Examiner notes that claim 5 depends on claim 4; however, claim 5 recites the limitation “triggering an erasure of the audiovisual content in the first set of audiovisual content at an electronic device in the set of electronic devices based on the determination that the audiovisual content in the first set of audiovisual content is relevant to the topic”. This limitation recites an additional element that amounts to significantly more than the claimed abstract idea in claim 4 and therefore is not rejected under 35 U.S.C. 101.
Claim 8:
Step 1: Claim 8 is directed to a method; therefore, the claim does fall within one of the four statutory categories (i.e., process, machine, manufacture, or composition of matter).
Step 2A, Prong 1: Claim 8 recites the following abstract ideas:
ranking the first set of audiovisual content for the topic based on the first set of predictive features (mental step directed to observation, evaluation – a person could rank a set of observed audiovisual content for a topic in their mind based on an observed or mentally determined set of predictive features).
Step 2A, Prong 2: as claim 8 depends on claim 1, claim 8 recites the following additional elements from the limitations in claim 1:
training, by at least one processor, the Al engine for the topic using data from a data source, wherein the topic is associated with a first geolocation; first receiving a first set of predictive features for the topic from the trained Al engine; transmitting the first set of predictive features for the topic to a set of electronic devices; second receiving a first set of audiovisual content captured by the set of electronic devices based on the first set of predictive features for the topic; retraining the AI engine based on the first set of audiovisual content; and providing a reward to an electronic device in the set of electronic devices based on the ranking.
As the claims do not recite any particular AI engine nor technical steps for training an AI engine, the AI engine is interpreted as a generic computer component and the training and retraining steps are interpreted as generic computer activity. Receiving a set of predictive features from a trained AI engine is interpreted as insignificant extra-solution activity directed to mere data gathering. Transmitting a set of predictive features to a set of electronic devices is interpreted as insignificant extra-solution activity directed to mere data gathering. Receiving a set of audiovisual content from a set of electronic devices is interpreted as insignificant extra-solution activity directed to mere data gathering. Providing a reward to an electronic device is interpreted as insignificant extra-solution activity directed to mere data gathering. These additional elements do not integrate the abstract idea into a practical application.
Step 2B: as claim 8 depends on claim 1, claim 8 recites the following additional elements from the limitations in claim 1:
training, by at least one processor, the Al engine for the topic using data from a data source, wherein the topic is associated with a first geolocation; first receiving a first set of predictive features for the topic from the trained Al engine; transmitting the first set of predictive features for the topic to a set of electronic devices; second receiving a first set of audiovisual content captured by the set of electronic devices based on the first set of predictive features for the topic; retraining the AI engine based on the first set of audiovisual content; and providing a reward to an electronic device in the set of electronic devices based on the ranking.
As the claims do not recite any particular AI engine nor technical steps for training an AI engine, the AI engine is interpreted as a generic computer component and the training and retraining steps are interpreted as generic computer activity. Receiving a set of predictive features from a trained AI engine is interpreted as well-understood, routine, conventional activity directed to transmitting and receiving data over a network. Transmitting a set of predictive features to a set of electronic devices is interpreted as well-understood, routine, conventional activity directed to transmitting and receiving data over a network. Receiving a set of audiovisual content from a set of electronic devices is interpreted as well-understood, routine, conventional activity directed to transmitting and receiving data over a network. Providing a reward to an electronic device is interpreted as well-understood, routine, conventional activity directed to transmitting and receiving data over a network. These additional elements do not amount to significantly more (see MPEP 2106.05(d)(II) and MPEP 2106.05(g)).
Claim 12 is a system claim and its limitation is included in claim 4. The only difference is that claim 12 requires a system comprising a memory and at least one processor, which are interpreted as generic computer components used to merely apply the abstract idea as identified in the analysis of claim 4 (see MPEP 2106.05(f)). Therefore, Claim 12 is rejected for the same reasons as claim 4.
Claim 15 is a system claim and its limitation is included in claim 8. The only difference is that claim 15 requires a system comprising a memory and at least one processor, which are interpreted as generic computer components used to merely apply the abstract idea as identified in the analysis of claim 8 (see MPEP 2106.05(f)). Therefore, claim 15 is rejected for the same reasons as claim 8.
Claim 18 is a non-transitory computer-readable device claim and its limitation is included in claim 4. The only difference is that claim 18 requires a non-transitory computer-readable device, which is interpreted as a generic computer component used to merely apply the abstract idea as identified in the analysis of claim 4 (see MPEP 2106.05(f)). Therefore, claim 18 is rejected for the same reasons as claim 4.
Claim Rejections - 35 USC § 102
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.
(a)(2) the claimed invention was described in a patent issued under section 151, or in an application for patent published or deemed published under section 122(b), in which the patent or application, as the case may be, names another inventor and was effectively filed before the effective filing date of the claimed invention.
Claims 1-4 and 6-20 are rejected under 35 U.S.C. 102(a)(2) as being anticipated by Kempel et al (US 20180322749 A1, herein Kempel).
Regarding claim 1, Kempel teaches a computer-implemented method (para. [0002] recites “The present disclosure pertains to monitoring systems and devices, and more specifically to systems and methods for providing automated personalized monitoring and other interactions”) for training an artificial intelligence (AI) engine for a topic, comprising:
training, by at least one processor, the Al engine for the topic using data from a data source, wherein the topic is associated with a first geolocation (para. [0030] recites “In an initial training period, Machine Learning system 117 is provided with training data, or sets of monitoring data that are known to contain indications of threats and potential threats. On the basis of this training data, Machine Learning system 117 forms an initial pattern-recognition system for classifying monitoring data”. Para. [0041] recites “Coupled to monitoring application 182 are five additional component groupings which permit monitoring application 182 to collect data transmitted, received, or generated by computing device 180a. Such data can include, without limitation, the contents of text messages, the contents of voice calls, keyboard inputs, user inputs/interactions with one or more third party applications 183, GPS or position information from a location tracking system 184” (i.e., training a model for a topic, such as personal safety, using sourced sensor data associated with a subject’s location));
first receiving a first set of predictive features for the topic from the trained Al engine; transmitting the first set of predictive features for the topic to a set of electronic devices (para. [0030] recites “In an initial training period, Machine Learning system 117 is provided with training data, or sets of monitoring data that are known to contain indications of threats and potential threats. On the basis of this training data, Machine Learning system 117 forms an initial pattern-recognition system for classifying monitoring data” (i.e., receiving known indications of potential threats, or predictive features, from an initial training step). Para. [0031] recites “This initial pattern-recognition system is then transmitted to Threat Analysis system 116, thereby initializing it to classify incoming monitoring data and identify threats and potential threats”. Fig. 1 and para. [0032] recites “Threat Response control system 114 is operative to receive indications of threats and potential threats identified by Threat Analysis system 116” (i.e., transmitting the initial predictions from the analysis system comprising the machine learning model to a control system connected to a series of external devices as shown in fig. 1));
second receiving a first set of audiovisual content captured by the set of electronic devices based on the first set of predictive features for the topic (para. [0029] recites “Threat Analysis system 116 may require additional information to assist in making a threat-level determination. Threat Analysis system 116 may have direct access to suitable sensors or other monitoring data collection devices, for example by way of the sensors and data acquisition means 190a, 190b” (i.e., a set of electronic devices capable of capturing audiovisual content for the analysis system). Para. [0031] recites “Every time Threat Analysis system 116 identifies a threat or a potential threat, it is saved to database 112, where it can be associated with one or more of the corresponding Subject's profile and a knowledge base for Machine Learning system 117”. Para. [0041] recites “As illustrated in FIG. 1, computing device 180a includes a monitoring application 182 which acts as a bridge or connection point between Subject 150a and threat monitoring and response system 100. Coupled to monitoring application 182 are five additional component groupings which permit monitoring application 182 to collect data transmitted, received, or generated by computing device 180a. Such data can include, without limitation. . . pictures and videos from an audiovisual capture system 186” (i.e., receiving additional audiovisual content from additional external devices based on the ongoing model updates));
and retraining the AI engine based on the first set of audiovisual content (para. [0031] recites “Every time Threat Analysis system 116 identifies a threat or a potential threat, it is saved to database 112, where it can be associated with one or more of the corresponding Subject's profile and a knowledge base for Machine Learning system 117. These updates provide a continuous source of additional training data for Machine Learning system 117, thereby allowing Threat Analysis system 116 to be refined and improved based on real world conditions and outcomes” (i.e., updating, or retraining, the model based on continuously received information, such as the audiovisual information from at least para. [0041])).
Regarding claim 2, Kempel teaches the computer-implemented method of claim 1 as mentioned above, wherein the second receiving comprises: receiving the first set of audiovisual content captured by the set of electronic devices based on a set of user characteristics (para. [0077] recites “Video or image data from the one or more video cameras can be used to better locate the requesting Subject once the drone has navigated to the general area defined by the current location of the Subject as received by the command center. For example, computer vision and one or more known characteristics of the requesting Subject (e.g. height, sex, hair color, eye color, reference images, etc.) can be utilized in combination such that the drone can recognize the requesting Subject in the scene captured by its video cameras and subsequently navigate to a desired position relative to the requesting Subject” (i.e., user characteristics can be determined from the received audiovisual content)).
Regarding claim 3, Kempel teaches the computer-implemented method of claim 1 as mentioned above, wherein the second receiving comprises: receiving audiovisual content in the first set of audiovisual content from an anonymous user of an electronic device in the set of electronic devices (para. [0077] recites “In some embodiments, the drone might only be equipped with a facial detection system, i.e. locate facial data in a given image frame, such that the drone can transmit this anonymous facial data to the threat monitoring and response system 100 and/or the monitoring application 182, which may be better equipped to handle the relatively processor-intensive task of performing facial recognition on the anonymous facial data detected by the drone” (i.e., receiving audiovisual content from a detected anonymous individual)).
Regarding claim 4, Kempel teaches the computer-implemented method of claim 1 as mentioned above, further comprising: determining, using the trained AI engine, that audiovisual content in the first set of audiovisual content is relevant to the topic based on the first set of predictive features and a second geolocation associated with the first set of audiovisual content (para. [0031] recites “Every time Threat Analysis system 116 identifies a threat or a potential threat, it is saved to database 112, where it can be associated with one or more of the corresponding Subject's profile and a knowledge base for Machine Learning system 117. Once a potential threat has been resolved, its entry in database 112 can be updated to indicate whether or not the potential threat was in fact a threat. In other words, the entry is updated to indicate whether or not (or the degree to which) Threat Analysis system 116 was correct”. Para. [0033] recites “Threat Response control system 114 may additionally consult an RCC 130 (i.e., regional command center) that is most closely associated with the Subject experiencing the potential threat” (i.e., determining whether the model was able to determine that the input audiovisual content was relevant to a topic, such as a safety threat, based on a given region, or location of the received content)).
Regarding claim 6, Kempel teaches the computer-implemented method of claim 1 as mentioned above, further comprising: triggering an intervention action based on a threshold number of predictive features of the first set of predictive features being present in audiovisual content in the first set of audiovisual content, wherein the audiovisual content in the first set of audiovisual content is captured by an electronic device in the set of electronic devices (para. [0043] recites “one or more biometric measurements can be used to assess a stress level of Subject 150a, such that these stress levels can function as a trigger to deploy the personal safety drone to capture and transmit to threat monitoring and response system 100 additional information regarding any potential threat or events that may have caused the unusually high stress levels” (i.e., determining whether assessed stress levels, or predictive features, should trigger an intervention action such as a drone launch). Para. [0051] recites “Subject 150a can use monitoring application 182 to program or otherwise configure the personal safety drone with a custom launch sequence as desired. For example, Subject 150a might input his height into monitoring application 182 and specify that the personal safety drone should launch to a position that is five feet above him and four feet in front of him and immediately begin streaming audiovisual data to a predetermined list of recipients (in addition to streaming audiovisual data to threat monitoring and response system 100). Para. [0051] recites “the personal safety drone launch can cause automatic activation of monitoring application 182 on the computing device 180a associated with Subject 150a. Various other launch signals can be employed by the personal safety drone to trigger computing device 180a to launch monitoring application 182” (i.e., received audiovisual content can also be a factor in triggering an intervention action)); and
notifying the electronic device in the set of electronic devices of the triggered intervention (para. [0043] recites “a personal safety drone could be deployed to determine whether the biometric sensor is providing a faulty reading or if Subject 150a is experiencing a significant cardiac distress event” (i.e., notifying an external device of a triggered intervention status, such as notifying the external drone device to trigger)).
Regarding claim 7, Kempel teaches the computer-implemented method of claim 1 as mentioned above, further comprising: receiving a second set of predictive features for the topic from the retrained Al engine; transmitting the second set of predictive features for the topic to the set of electronic devices (para. [0029] recites “Threat Analysis system 116 may require additional information to assist in making a threat-level determination. Threat Analysis system 116 may have direct access to suitable sensors or other monitoring data collection devices, for example by way of the sensors and data acquisition means 190a, 190b”. Para. [0031] recites “Every time Threat Analysis system 116 identifies a threat or a potential threat, it is saved to database 112, where it can be associated with one or more of the corresponding Subject's profile and a knowledge base for Machine Learning system 117”. Para. [0032] recites “Threat Response control system 114 is operative to receive indications of threats and potential threats identified by Threat Analysis system 116” (i.e. receiving additional, or a second set of, audiovisual content captured from an external device to analyze for predictive features for a topic, such as a safety alert, and transmitting the initial predictions to a device such as a threat control response system));
receiving a second set of audiovisual content captured by a portion of the set of electronic devices based on the second set of predictive features for the topic (para. [0029] recites “Threat Analysis system 116 may require additional information to assist in making a threat-level determination. Threat Analysis system 116 may have direct access to suitable sensors or other monitoring data collection devices, for example by way of the sensors and data acquisition means 190a, 190b”. Para. [0031] recites “Every time Threat Analysis system 116 identifies a threat or a potential threat, it is saved to database 112, where it can be associated with one or more of the corresponding Subject's profile and a knowledge base for Machine Learning system 117”. Para. [0041] recites “As illustrated in FIG. 1, computing device 180a includes a monitoring application 182 which acts as a bridge or connection point between Subject 150a and threat monitoring and response system 100. Coupled to monitoring application 182 are five additional component groupings which permit monitoring application 182 to collect data transmitted, received, or generated by computing device 180a. Such data can include, without limitation. . . pictures and videos from an audiovisual capture system 186” (i.e., receiving additional, or a second set of, audiovisual content from a device based on the ongoing model updates)); and
retraining the Al engine based on the second set of audiovisual content (para. [0031] recites “Every time Threat Analysis system 116 identifies a threat or a potential threat, it is saved to database 112, where it can be associated with one or more of the corresponding Subject's profile and a knowledge base for Machine Learning system 117. These updates provide a continuous source of additional training data for Machine Learning system 117, thereby allowing Threat Analysis system 116 to be refined and improved based on real world conditions and outcomes” (i.e., updating, or retraining, the model based on continuously received, or a second set of, information, such as the audiovisual information from at least para. [0041])).
Regarding claim 8, Kempel teaches the computer-implemented method of claim 1 as mentioned above, further comprising: ranking the first set of audiovisual content for the topic based on the first set of predictive features; and providing a reward to an electronic device in the set of electronic devices based on the ranking (para. [0025] recites “Confidence levels can be calculated in order to reflect the probability that Threat Analysis system 116 has correctly identified a threat, or the probability that a potential threat at least exists. The confidence level can be utilized by Threat Response control system 114 to better determine an appropriate threat response type or level, e.g. balancing between a worst case scenario and a best case scenario possible given the confidence level”. Para. [0026] recites “In some embodiments, various thresholds on the confidence level can be set and utilized to trigger supplemental analysis beyond that offered by Threat Analysis system 116. For example, a 50% confidence level might indicate that the Threat Analysis system 116 obtained inconclusive results, and supplemental analysis is necessary to see if the confidence level can be adjusted either up or down” (i.e., providing a confidence, or ranking, of the output of the model, or the predictive features, and providing an indication of success, or a reward, based on the determined confidence)).
Claim 9 is a system claim and its limitation is included in claim 1. The only difference is that claim 9 requires a system. Therefore, claim 9 is rejected for the same reasons as claim 1.
Claim 10 is a system claim and its limitation is included in claim 2. Claim 10 is rejected for the same reasons as claim 2.
Claim 11 is a system claim and its limitation is included in claim 3. Claim 11 is rejected for the same reasons as claim 3.
Claim 12 is a system claim and its limitation is included in claim 4. Claim 12 is rejected for the same reasons as claim 4.
Claim 13 is a system claim and its limitation is included in claim 6. Claim 13 is rejected for the same reasons as claim 6.
Claim 14 is a system claim and its limitation is included in claim 7. Claim 14 is rejected for the same reasons as claim 7.
Claim 15 is a system claim and its limitation is included in claim 8. Claim 15 is rejected for the same reasons as claim 8.
Claim 16 is a non-transitory computer-readable device claim and its limitation is included in claim 1. The only difference is that claim 16 requires a non-transitory computer-readable device. Therefore, claim 16 is rejected for the same reasons as claim 1.
Claim 17 is a non-transitory computer-readable device claim and its limitation is included in claim 2. Claim 17 is rejected for the same reasons as claim 2.
Claim 18 is a non-transitory computer-readable device claim and its limitation is included in claim 4. Claim 18 is rejected for the same reasons as claim 4.
Claim 19 is a non-transitory computer-readable device claim and its limitation is included in claim 6. Claim 19 is rejected for the same reasons as claim 6.
Claim 20 is a non-transitory computer-readable device claim and its limitation is included in claim 7. Claim 20 is rejected for the same reasons as claim 7.
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 5 is rejected under 35 U.S.C. 103 as being unpatentable over Kempel et al (US 20180322749 A1, herein Kempel) in view of LaRosa et al (US 20130338806 A1, herein LaRosa).
Regarding claim 5, Kempel teaches the computer-implemented method of claim 4 as mentioned above.
However, while Kempel does teach determining that audiovisual content is relevant to a topic (see at least para. [0031]), Kempel does not explicitly teach triggering an erasure of the audiovisual content in the first set of audiovisual content at an electronic device in the set of electronic devices based on the determination that the audiovisual content in the first set of audiovisual content is relevant [to the topic].
LaRosa teaches triggering an erasure of the audiovisual content in the first set of audiovisual content at an electronic device in the set of electronic devices based on the determination that the audiovisual content in the first set of audiovisual content is relevant [to the topic] (para. [0049] recites “An already-existing policy outcome can include, but is not limited to, muting an entire audio track for a video, blocking playback of a video in one or more geographical locations (e.g., countries), not allowing a video to display advertisements, etc. Additionally, the interface component 302 can notify a user that the already-existing policy outcome(s) can be reversed by removing (e.g., erasing) the relevant sound recordings (e.g., copyrighted songs) from the video”. Para. [0054] recites “The user can remove at least one of the one or more songs 608a-n from the video 604 (e.g. the mixed audio recording) by selecting a corresponding remove button 610a-n” (i.e., triggering a removal, or erasure, of audiovisual content based on a determination of relevant content)).
Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to combine these teachings by utilizing the method of removing relevant content from LaRosa to remove potentially sensitive information from the audiovisual content captured by the system from Kempel. Kempel teaches in at least paragraph [0077] that sensitive information can be removed from captured audiovisual content. While this paragraph of Kempel uses image redaction as an example, one of ordinary skill in the rat would recognize that sensitive audio information could also be removed from Kempel’s captured content using a method such as the one taught by LaRosa.
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure.
US 20180322103 A1 (Yeo et al) teaches a method for extracting audiovisual features from images and other digital components to identify candidate images.
US 20170171294 A1 (Kennedy et al) teaches a method for automatically determining media content to communicate to a user based on the user's location, other users’ relationships to the location, and user generated media content.
US 20160299899 A1 (Logachev et al) teaches a method for generating a user-specific ranking model based on content specific features such as geolocation.
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/L.M.F./ Examiner, Art Unit 2147
/VIKER A LAMARDO/Supervisory Patent Examiner, Art Unit 2147
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