Notice of Pre-AIA or AIA Status
The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA .
Double Patenting
The nonstatutory double patenting rejection is based on a judicially created doctrine grounded in public policy (a policy reflected in the statute) so as to prevent the unjustified or improper timewise extension of the “right to exclude” granted by a patent and to prevent possible harassment by multiple assignees. A nonstatutory double patenting rejection is appropriate where the conflicting claims are not identical, but at least one examined application claim is not patentably distinct from the reference claim(s) because the examined application claim is either anticipated by, or would have been obvio us 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.
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Claims1-17 rejected on the ground of nonstatutory double patenting as being unpatentable over claims 1-12 of U.S. Patent No. 12,321,493. Although the claims at issue are not identical, they are not patentably distinct from each other because the Present Claimed invention is substantially a minor variation on the unamended claims of application 17/904,652 (now U.S. Patent No. 12,321,493).
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)(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.
Claim(s) 1-5, 7, and 9-17 is/are rejected under 35 U.S.C. 102(a)(2) as being antedated by United States Patent Application Publication No.: US 2016/0379047 A1 (NATAN et al.).
As Per Claim 1: NATAN et al. teaches: A computer-implemented method for interactive communication of a user device with a server, the method comprising:
- providing, on the user device, a notification to a user of the user device;
- acquiring reaction data indicative of a reaction of the user to the notification; and
- determining, based on the acquired reaction data,
(NATAN et al., Paragraph [0011], “Emotion detection systems disclosed herein actively protect the privacy and security of monitored subjects. This provision of protection differentiates the systems described herein from conventional emotion detectors. In some example embodiments, a self-contained emotion detection device protects the identity of a subject while providing anonymous data descriptive of the subject's emotional state to other devices within the emotion detection system. In these examples, the emotion detection device protects the identity of the subject by isolating raw image data acquired by the device exclusively within storage locations encapsulated within the device. To provide the anonymous data, the emotion detection device first processes the raw image data to identify landmark points within an image of the subject's face. Next, the emotion detection device normalizes the landmark points to a generic face and transmits an anonymous data set descriptive of the normalized landmark points to the other devices for subsequent processing. In some examples, the other devices process the anonymous data set to identify and record the emotional state of the subject in association with information descriptive of stimuli being presented to the subject. This stimuli may include non-interactive content (e.g., books, television, movies, radio programs, music, advertisements, etc.). The stimuli may also include interactive content (e.g., games, shopping, etc.). In some examples, the other devices within the emotion detection system attempt to affect the emotional state of the subject by adjusting the stimuli (e.g., adjusting a price, plotline, music genre, etc.). While various example embodiments provided herein refer to facial images, other target features can be used as well to assess emotional state of a subject, such as voice recordings, body posture and/or gesture images, and biometric data such as heart rate and blood pressure, to name a few examples. As will be appreciated, the techniques provided herein can be used to encapsulate any such target raw data within the device, identify landmark points within that data, and normalize the landmark points to a generic model (such as a generic voice model that repeats the user's inflections and raised tones using a generic voice, or a generic torso model that repeats the user's body language and/or gestures using a generic body). In any such example cases, the anonymous data set descriptive of the normalized landmark points can be transmitted to the other devices for subsequent processing.”.).
(NATAN et al., Paragraph [0016], “Thus, and in accordance with some examples of the present disclosure, emotion detection devices and systems are provided, wherein output data indicative of emotional states of a subject is anonymous (i.e., the output data does not indicate the personal identity of the subject, such that, for example, the subject's actual identify is not included in the output data). Such emotion detection devices and systems differ from conventional emotion detectors at least by generating anonymous data by, for example, normalizing data to a model of a generic subject. In some such examples, this anonymous data is the sole output provided by the emotion detection device. In these examples, the emotion detection device deletes information that may be used to identify a subject (e.g., information descriptive of the subject's face, voice, body posture and gesturing or other detectable, identifying traits) prior to providing the anonymous data. In this way, these examples provide additional protection to the privacy and security of the subject being monitored.”.).
(NATAN et al., Paragraph [0017], “In some examples, the emotion detection devices are incorporated into emotion detection systems that include additional features. For instance, some examples comprise an emotion detection system that includes one or more emotion detection devices, one or more remote devices that analyze anonymous data transmitted by the emotion detection devices, and a network coupling the emotion detection devices to the remote devices. In these examples, the remote devices receive the anonymous data and mine the anonymous data to classify the emotional states of individual subjects. Emotion identifiers indicating the emotional state of individuals are then securely stored for subsequent processing.”.).
(NATAN et al., Paragraph [0048], “Continuing this example, the remote device 404 executes the act 502 to receive the anonymous data set 304 and records the data in a data store local to the remote device. Next, the remote device 404 executes the act 506 to determine whether recording of the anonymous data triggers an additional action by referencing the values of one or more configurable parameters. If the remote device 404 determines that no addition action has been triggered, processing terminates here. However, in this example, the remote device 404 determines that an additional action has been triggered and executes the act 508 to transmit a stimulus message 600 to the stimulus controller 406. The stimulus message 600 may include a request to present additional stimulus predicted to transition the emotional state of the subject to a new emotional state. In response to receiving the stimulus message 600, the stimulus controller 406 executes the act 510 to generate the stimulus 414. At this point, the activity described above may repeat with the emotion detection 402 executing the act 204 to poll for an image of the subject 410 at a time shorty after presentation of the stimulus 414 to the subject 410.”.).
- a sentiment score for transmission to the server,
- wherein the sentiment score is indicative of a sentiment of the user in reaction to the notification.
(NATAN et al., Paragraph [0051], “Another example of a specialized emotion detection system is directed to online shopping. In this example, the emotion detection device 402 is positioned to view the subject 410 as the subject 410 receives the stimulus 412 by browsing websites in search of items to purchase. The remote device 404 records the anonymous data sets and further analyzes them to determine which items or prices caused positive and negative emotional reactions in the subject 410. Further, in this example, the stimulus controller 406 may present additional stimulus (e.g., the stimulus 414) in predefined situations. For instance, where the subject 410 expressed positive emotion when presented with an item without pricing information and subsequently expressed negative emotion when presented with a price, the remote device 404 may transmit a stimulus message 600 to discount the price by a specified percentage. In response to receiving the stimulus message 600, the stimulus controller 406 may transmit additional stimulus 414 in the form of the discounted price.”.).
(NATAN et al., Paragraph [0057], “Another example of a specialized emotion detection system is directed to automobile operation. In this example, the emotion detection device 402 is positioned to view the subject 410 as the subject 410 receives the stimulus 412 by driving an automobile and reviewing content provided by an in-vehicle entertainment system. The remote device 404 records the anonymous data sets and further analyzes them to determine which events (weather and traffic conditions, songs played on a radio, etc.) caused positive and negative emotional reactions in the subject 410. Further, in this example, the stimulus controller 406 may present additional stimulus (e.g., the stimulus 414) in predefined situations. For instance, where the subject 410 expressed negative emotion when presented with a weather or traffic condition, the remote device 404 may transmit a stimulus message 600 to calm the subject 410. In response to receiving the stimulus message 600, the stimulus controller 406 may provide additional stimulus 414 in the form of soothing music or a soothing message to relax the subject 410. In another example, where the subject 410 expresses a lack of emotion for a period of time that exceeds the value of a configurable parameter, the remote device 404 may transmit a stimulus message 600 to energize the subject 410. In response to receiving the stimulus message 600, the stimulus controller 406 may provide additional stimulus 414 in the form of energizing music, message or alarm to energize the subject 410.”.).
As Per Claim 2: The rejection of claim 1 is incorporated and further NATAN et al. teaches:
- acquiring the reaction data comprises receiving, with the user device, user-side sensor data from at least one user-side sensor communicatively coupled with the user device.
(NATAN et al., Paragraph [0027], “In act 206, the device determines whether any of the one or more frames includes an image of a face of a subject. If so, the device proceeds to act 208. Otherwise, the device proceeds to the act 218. In the act 208, the device stores the frame for subsequent processing. Also, in at least one example, the device deactivates the camera within the act 208 as a security measure. By deactivating the camera in the act 208, the device helps ensure that additional image data is not acquired by any authorized processes executing on the device.”.).
As Per Claim 3: The rejection of claim 1 is incorporated and further NATAN et al. teaches:
- deriving, with the user device, the reaction data from the received user-side sensor data of the at least one user-side sensor.
(NATAN et al., Paragraph [0027], “In act 206, the device determines whether any of the one or more frames includes an image of a face of a subject. If so, the device proceeds to act 208. Otherwise, the device proceeds to the act 218. In the act 208, the device stores the frame for subsequent processing. Also, in at least one example, the device deactivates the camera within the act 208 as a security measure. By deactivating the camera in the act 208, the device helps ensure that additional image data is not acquired by any authorized processes executing on the device.”.).
(NATAN et al., Paragraph [0028], “In act 210, the device analyzes the image to identify and store data indicative of an emotional state of the subject. In some examples, the device executes Intel® Realsense™ technology to identify landmark points within the image of the face. Landmark points are a collection of points that specify the identity and orientation of facial features such as lips, eyebrows, eye lids, etc. In one example, the device identifies 78 landmark points that, in combination, indicate the emotional state of the subject.”.).
As Per Claim 4: The rejection of claim 1 is incorporated and further NATAN et al. teaches:
- the reaction data is acquired by the user device based on sensor data of at least one sensor of the user device and based on user-side sensor data of at least one user-side sensor arranged in an environment of the user device.
(NATAN et al., Paragraph [0036], “FIG. 4 illustrates an emotion detection system 400 configured to monitor and, optionally, affect the emotional state of a subject 410. As shown, the emotion detection system 400 includes an emotion detection device 402, a remote device 404, a stimulus controller 406, and a network 408. The stimulus controller 406 may generate stimuli including stimulus 412 and stimulus 414. The emotion detection device 402 may include, for example, a PERS as described above with reference to FIGS. 1-3. The remote device may include any programmable device, such as any of the systems described below with reference to FIGS. 7 and 8. The communication network 408 may include any communication network through which systems may exchange information. For example, the network 408 may be a public network, such as the Internet, and may include other public or private networks such as LANs, WANs, extranets and intranets. As shown in FIG. 4, the emotion detection device 402, the remote device 404, and, optionally, the stimulus controller 406 are connected to and communicate data via the network 408.”.).
As Per Claim 5: The rejection of claim 2 is incorporated and further NATAN et al. teaches:
- the at least one user-side sensor is at least one of a camera, an acoustic sensor, an accelerometer, a motion sensor, a gyroscope, a capacitive sensor, a touch sensor, a piezoelectric sensor, a piezoresistive sensor, a Hall sensor, a contact blood pressure sensor, a photoplethysmography sensor, an oximeter, a laser sensor, a heart rate sensor, a respiratory sensor, an air flow sensor, an air pressure sensor, a temperature sensor, an electrochemical gas sensor, an ultrasonic sensor, an acoustic resonance sensor, an optical sensor, an infrared sensor, a near field sensor, a time- of-flight sensor, a radar sensor, and a bio-impedance sensor.
(NATAN et al., Paragraph [0021], “This emotion detection device may include a camera, a face image processing component, data storage, and a data transmission component. In some examples, the camera acquires image data descriptive of the subject's face. The face image processing component processes the image data to generate an anonymous set of data descriptive of the physical state of the subject's face. This anonymous data set may be further analyzed to determine details regarding the emotional state of the subject but cannot be used to determine the identity of the subject. This device can be readily extrapolated to other emotion-indicating features. For instance, in another embodiment, the emotion detection device may include a microphone, a voice processing component, data storage, and a data transmission component. In such examples, the microphone acquires voice data descriptive of the subject's verbal utterances. The voice processing component processes the voice data to generate an anonymous set of data descriptive of the state of the subject's voice (with respect to inflection, loudness, etc). This anonymous data set may be further analyzed to determine details regarding the emotional state of the subject but cannot be used to determine the identity of the subject. In other examples, the emotion detection device may include a camera, a body posture, and gesture image processing component, data storage, and a data transmission component. In such examples, the camera acquires body posture and/or gesture data descriptive of the subject's body posture and gesturing state. The image processing component processes the image data to generate an anonymous set of data descriptive of the physical state of the subject's body and/or gesturing. This anonymous data set may be further analyzed to determine details regarding the emotional state of the subject but cannot be used to determine the identity of the subject. Numerous other variations will be apparent in light of this disclosure. The choice of specific target emotion-indicating features will depend on factors such as desired computational burden in processing the data to identify emotional state of the subject. Any such target emotion-indicating features can be analyzed and used on their own as variously provided herein, or in conjunction with other target emotion-indicating features in effort to gain a more comprehensive understanding of a given subject's emotional state (e.g., face and voice, or face and gesture, etc). In any such cases, raw emotion-indicating data is translated into an anonymous equivalent, and the raw data itself can be discarded so as to protect the identity of the subject..).
As Per Claim 7: The rejection of claim 1 is incorporated and further NATAN et al. teaches:
- the sentiment score is an anonymized numerical measure indicative of the reaction of the user to the notification.
(NATAN et al., Paragraph [0051], “Another example of a specialized emotion detection system is directed to online shopping. In this example, the emotion detection device 402 is positioned to view the subject 410 as the subject 410 receives the stimulus 412 by browsing websites in search of items to purchase. The remote device 404 records the anonymous data sets and further analyzes them to determine which items or prices caused positive and negative emotional reactions in the subject 410. Further, in this example, the stimulus controller 406 may present additional stimulus (e.g., the stimulus 414) in predefined situations. For instance, where the subject 410 expressed positive emotion when presented with an item without pricing information and subsequently expressed negative emotion when presented with a price, the remote device 404 may transmit a stimulus message 600 to discount the price by a specified percentage. In response to receiving the stimulus message 600, the stimulus controller 406 may transmit additional stimulus 414 in the form of the discounted price.”.).
As Per Claim 9: The rejection of claim 1 is incorporated and further NATAN et al. teaches:
- the determined sentiment score is transmitted from at least one of the user device and a user-side device communicatively coupled with the user device.
(NATAN et al., Paragraph [0031], “In act 216, the device transmits the anonymous data set to an address specified by the value of a predefined configurable parameter. This address may be a local address, i.e., an address of a component that is integral to the device, or a remote address, i.e., an address of a component that is not integral to the device. In act 218, the device determines whether shutdown in imminent. If so, the detection process 200 ends. Otherwise, the detection process 200 proceeds to the act 204.”.).
As Per Claim 10: The rejection of claim 1 is incorporated and further NATAN et al. teaches:
- determining the sentiment score comprises: determining an intermediate sentiment score based on the acquired reaction data, and anonymizing the intermediate sentiment score, thereby generating the sentiment score.
(NATAN et al., Paragraph [0011], “Emotion detection systems disclosed herein actively protect the privacy and security of monitored subjects. This provision of protection differentiates the systems described herein from conventional emotion detectors. In some example embodiments, a self-contained emotion detection device protects the identity of a subject while providing anonymous data descriptive of the subject's emotional state to other devices within the emotion detection system. In these examples, the emotion detection device protects the identity of the subject by isolating raw image data acquired by the device exclusively within storage locations encapsulated within the device. To provide the anonymous data, the emotion detection device first processes the raw image data to identify landmark points within an image of the subject's face. Next, the emotion detection device normalizes the landmark points to a generic face and transmits an anonymous data set descriptive of the normalized landmark points to the other devices for subsequent processing. In some examples, the other devices process the anonymous data set to identify and record the emotional state of the subject in association with information descriptive of stimuli being presented to the subject. This stimuli may include non-interactive content (e.g., books, television, movies, radio programs, music, advertisements, etc.). The stimuli may also include interactive content (e.g., games, shopping, etc.). In some examples, the other devices within the emotion detection system attempt to affect the emotional state of the subject by adjusting the stimuli (e.g., adjusting a price, plotline, music genre, etc.). While various example embodiments provided herein refer to facial images, other target features can be used as well to assess emotional state of a subject, such as voice recordings, body posture and/or gesture images, and biometric data such as heart rate and blood pressure, to name a few examples. As will be appreciated, the techniques provided herein can be used to encapsulate any such target raw data within the device, identify landmark points within that data, and normalize the landmark points to a generic model (such as a generic voice model that repeats the user's inflections and raised tones using a generic voice, or a generic torso model that repeats the user's body language and/or gestures using a generic body). In any such example cases, the anonymous data set descriptive of the normalized landmark points can be transmitted to the other devices for subsequent processing.”.).
As Per Claim 11: The rejection of claim 10 is incorporated and further NATAN et al. teaches:
- the sentiment score is anonymized based on normalizing the intermediate sentiment score with a reference sentiment score.
(NATAN et al., Paragraph [0085], “Example 3 includes the subject matter of any of the preceding Examples, wherein the face image processing module is configured to: analyze the image data at least in part by normalizing a data set to a generic face; and store the anonymous data at least in part by storing the data set.”.).
As Per Claim 12: The rejection of claim 9 is incorporated and further NATAN et al. teaches:
- removing the intermediate sentiment score from the user device upon anonymizing the intermediate sentiment score.
(NATAN et al., Paragraph [0029], “In act 212, the device deletes the frame as an additional security measure. By deleting the frame in the act 212, the device limits the amount of time that the frame is stored and the amount of time that the frame and landmark points coexist in the storage of the device.”.).
As Per Claim 13: The rejection of claim 1 is incorporated and further NATAN et al. teaches:
- deleting the sentiment score upon transmitting the sentiment score to the server.
(NATAN et al., Paragraph [0029], “In act 212, the device deletes the frame as an additional security measure. By deleting the frame in the act 212, the device limits the amount of time that the frame is stored and the amount of time that the frame and landmark points coexist in the storage of the device.”.).
As Per Claim 14: The rejection of claim 1 is incorporated and further NATAN et al. teaches:
- acquiring the reaction data includes: capturing sensor data with at least one sensor of the user device, and deriving, with the user device, the reaction data from the captured sensor data of the at least one sensor of the user device.
(NATAN et al., Paragraph [0027], “In act 206, the device determines whether any of the one or more frames includes an image of a face of a subject. If so, the device proceeds to act 208. Otherwise, the device proceeds to the act 218. In the act 208, the device stores the frame for subsequent processing. Also, in at least one example, the device deactivates the camera within the act 208 as a security measure. By deactivating the camera in the act 208, the device helps ensure that additional image data is not acquired by any authorized processes executing on the device.”.).
As Per Claim 15: The rejection of claim 1 is incorporated and further NATAN et al. teaches:
- deriving, from the reaction data, at least one environmental parameter, wherein the at least one environmental parameter is indicative of an environment of the user affecting the sentiment of the user, wherein the sentiment score is determined based on the at least one environmental parameter.
(NATAN et al., Paragraph [0051], “Another example of a specialized emotion detection system is directed to online shopping. In this example, the emotion detection device 402 is positioned to view the subject 410 as the subject 410 receives the stimulus 412 by browsing websites in search of items to purchase. The remote device 404 records the anonymous data sets and further analyzes them to determine which items or prices caused positive and negative emotional reactions in the subject 410. Further, in this example, the stimulus controller 406 may present additional stimulus (e.g., the stimulus 414) in predefined situations. For instance, where the subject 410 expressed positive emotion when presented with an item without pricing information and subsequently expressed negative emotion when presented with a price, the remote device 404 may transmit a stimulus message 600 to discount the price by a specified percentage. In response to receiving the stimulus message 600, the stimulus controller 406 may transmit additional stimulus 414 in the form of the discounted price.”.).
(NATAN et al., Paragraph [0057], “Another example of a specialized emotion detection system is directed to automobile operation. In this example, the emotion detection device 402 is positioned to view the subject 410 as the subject 410 receives the stimulus 412 by driving an automobile and reviewing content provided by an in-vehicle entertainment system. The remote device 404 records the anonymous data sets and further analyzes them to determine which events (weather and traffic conditions, songs played on a radio, etc.) caused positive and negative emotional reactions in the subject 410. Further, in this example, the stimulus controller 406 may present additional stimulus (e.g., the stimulus 414) in predefined situations. For instance, where the subject 410 expressed negative emotion when presented with a weather or traffic condition, the remote device 404 may transmit a stimulus message 600 to calm the subject 410. In response to receiving the stimulus message 600, the stimulus controller 406 may provide additional stimulus 414 in the form of soothing music or a soothing message to relax the subject 410. In another example, where the subject 410 expresses a lack of emotion for a period of time that exceeds the value of a configurable parameter, the remote device 404 may transmit a stimulus message 600 to energize the subject 410. In response to receiving the stimulus message 600, the stimulus controller 406 may provide additional stimulus 414 in the form of energizing music, message or alarm to energize the subject 410.”.).
As Per Claim 16: Claim 16 is substantially a restatement of the method of claim 1 as a non-transitory computer-readable storage medium and is rejected under substantially the same reasoning.
As Per Claim 17: Claim 17 is substantially a restatement of the method of claim 1 as a user device and is rejected under substantially the same reasoning.
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.
Claim(s) 6 and 8 is/are rejected under 35 U.S.C. 103 as being unpatentable over United States Patent Application Publication No.: US 2016/0379047 A1 (NATAN et al.).
As Per Claim 6: The rejection of claim 2 is incorporated and further NATAN et al. does not explicitly teach the following limitation teaches:
- the user-side device is an aerosol-generating device.
However Examiner is giving Official Notice that the user-side device being an aerosol-generating device would be an obvious interchangeable variation readily implemented with expectations of success to one of ordinary skill in the art before the effective filing date of the claimed invention. The device being an aerosol-generating device in particular does not change the functional operation of the invention and aerosol-generating device would be the sort of product that the reaction of potential users would want to be evaluated for.
As Per Claim 8: The rejection of claim 1 is incorporated and further NATAN et al. does not explicitly teach the following limitation teaches:
- the sentiment score correlates with a reinforcement learning reward configured for being used by the server for training a reinforcement learning model implemented on the server.
However Examiner is giving Official Notice that the training of a reinforcement learning model would be an obvious interchangeable variation readily implemented with expectations of success to one of ordinary skill in the art before the effective filing date of the claimed invention. This would be an established variation on the NATAN et al.’s analysis inferences seen in paragraph 0043.
(NATAN et al., Paragraph [0043], “The particular mode of analysis and potential inferences drawn within the act 504 vary between examples. For instance, according to some examples, various data mining techniques are employed to determine (e.g., classify) an emotional state represented by one or more anonymous data sets. Examples of the data mining techniques that may be executed for this purpose include neural networks, k-nearest neighbor processes, and vector machines. In other examples, within the act 504, the system executes data mining prediction processes (e.g., logistical regression) to predict an emotional state of a subject or group of subjects based on a history of anonymous data sets received and processed by the system. In these examples, the system may create one or more summaries articulating these predictions for users of the system.”.).
Conclusion
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/BENJAMIN A KAPLAN/Examiner, Art Unit 2434
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