DETAILED ACTION
This office action is in response to application filed on 7/27/2023.
Claims 1 – 20 are pending.
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 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) 1, 3, 12, 13, 15 and 20 is/are rejected under 35 U.S.C. 103 as being unpatentable over Tukka et al (US 20190371342, hereinafter Tukka), in view of Bastide et al (US 20190171991, hereinafter Bastide), and further in view of Patton et al (US 20180276351, hereinafter Patton).
As per claim 1, Tukka discloses: A method, at an electronic device associated with a user, the method comprising:
obtaining, from one or more sensors, sensed data representing a sensed location of the user and at least one other human or at least one other device; (Tukka [0054]: “The location detection unit 304 can be configured to determine the information about the source location of the detected first event and the successive events, the location of the user 208/plurality of users (208a-208n) at the time of occurrence of the at least one of the first event and the successive events or the like. The location detection unit 304 determines the location information using the at least one of the device (such as a smart phone, a wearable device, a camera, an IoT device, motion sensors and so on) employed by the system 200 for monitoring the activities of the user and the devices 206.”)
defining a first proxemic probability density function (PDF) representing likelihood of interaction in a personal space of the user, the first proxemic PDF being defined using the sensed location of the user; (Tukka [0071]: “the user related information determination unit 306 determines whether the user 208 is talking to another user/the position of the user/the gestures being performed by the user 208 or the like at the time of the occurrence of the events. In an example herein, the user related information determination unit 306 determines the user 208 is talking to another user while walking. Based on the detection of the events and the user related information, the contextual probability estimation unit 308 estimates the contextual probability of initiating the voice interaction by the user 208 with the user interaction device 202 after wakeup.”)
generating an entropy metric representing likelihood of interaction between the user and the at least one other human or the at least one other device; (Tukka [0077]: “estimating, by the configuring device 204, the contextual probability of the user initiating the interaction with the user interaction device 202. On determining the occurrence of the at least one of the first event and the successive events, the configuring device 204 estimates the contextual probability. The configuring device 204 estimates the contextual probability based on the occurrence of the events and the context. The context can be determined using the context parameters such as, but not limited to, the user context, the user PLM data, the device context, the history of voice interactions, and history of sequence of events and so on… Using the correlation, the first event, and the successive events with the context, the configuring device 204 can predict with a confidence value that the likelihood of user initiating the voice conversation with the user interaction device 202. The confidence value indicates the contextual probability of initiating the voice conversation with the user interaction device 202.”)
and in response to the entropy metric exceeding a defined threshold, transitioning the electronic device from a default mode to an engaged mode, wherein in the engaged mode the electronic device is controlled to provide at least one output differently than in the default mode. (Tukka [0078]: “At step 408, the method includes configuring, by the configuring device 204, the dynamic wakeup time for switching the user interaction device 202 to the passive wakeup state. The configuring device 204 compares the estimated contextual probability with the pre-defined threshold value. On determining that the estimated contextual probability is not above the pre-defined threshold value, the configuring device 204 switches the user interaction device 202 to the sleep state. On determining that the contextual probability is above the pre-defined threshold, the configuring device 204 estimates the delta wakeup time duration for switching the user interaction device 202 to the passive wakeup state. In the passive wakeup state, the user interaction device 202 continues to be active in the background state for the delta wakeup time duration, listens to the user commands and provides the responses to the user 208 without any trigger word.”. Examiner notes that the response wakeup state is mapped to the claimed engaged mode, the output of the wakeup state is different than the sleep mode.)
Tukka did not explicitly disclose:
defining at least one other proxemic PDF representing likelihood of interaction with the at least one other human or the at least one other device, the at least one other proxemic PDF being defined using the sensed location of the at least one other human or the at least one other device;
wherein the entropy metric is determined by computing an overlap between the first proxemic PDF and the at least one other proxemic PDF;
However, Bastide teaches:
defining at least one other proxemic PDF representing likelihood of interaction with the at least one other human or the at least one other device, the at least one other proxemic PDF being defined using the sensed location of the at least one other human or the at least one other device; (Bastide [0027]: “Analysis module 116 determines when a roaming user is approaching an engaged user based on the signal strengths of corresponding client devices 114 stored in database system 118.”; [0028]: “When a roaming user is identified as approaching the engaged user, the probability of interaction between the roaming and engaged users is determined at step 215.”)
It would have been obvious for one of ordinary skill in the art before the effective filing date of the claimed invention to incorporate the teaching of Bastide into that Tukka in order to define a first proxemic probability density function (PDF) representing likelihood of interaction in a personal space of the user. Tukka [0060] teaches “the contextual probability estimation unit 308 can be configured to estimate the contextual probability of initiating the voice interaction by the at least one user 208a with the at least one user interaction device (202a) of the plurality of user interaction devices 202a-202n.”, Bastide [0027] – [0028] teaches probability can be established for each engaged user regarding their odds of interaction with a content, such combination would enhance the overall appeals of all references by allowing more accurate prediction of chances of interaction based on multiple factors, and is therefore rejected under 35 USC 103.
Patton teaches:
wherein the entropy metric is determined by computing an overlap between the first proxemic PDF and the at least one other proxemic PDF; (Patton [0116]: “determining the notification includes detecting the event in a geographic region (e.g., monitored location), identifying the vehicles associated with the geographic region (e.g., vehicles currently located within the geographic region, vehicles with an interaction region overlapping the geographic region, vehicles with interaction regions that will overlap the geographic region within a threshold period of time, etc.),”)
It would have been obvious for one of ordinary skill in the art before the effective filing date of the claimed invention to incorporate the teaching of Patton into that of Tukka and Bastide in order to have the entropy metric is determined by computing an overlap between the first proxemic PDF and the at least one other proxemic PDF. Patton [0116] has shown that the concept of event detection through identification of overlaps between 2 objects is commonly known and utilized, therefore one of ordinary skill in the art would know to apply a well-known correlation methods to determine the probability of interaction between a user and a device, and applicants has merely claimed the combination of known parts in the field to achieve predictable result of accurately determining predict interactions based on proximity and is therefore rejected under 35 USC 103.
As per claim 3, the combination of Tukka, Bastide and Patton further teach:
The method of claim 1, wherein the first proxemic PDF is defined to comprise two or more constituent PDFs, wherein each constituent PDF represents likelihood of a respective type of interaction in the personal space of the user. (Tukka [0071])
As per claim 12, the combination of Tukka, Bastide and Patton further teach:
The method of claim 1, wherein in the engaged mode the electronic device is controlled to adjust the at least one output proportionate to a value of the entropy metric. (Tukka [0078]: “At step 408, the method includes configuring, by the configuring device 204, the dynamic wakeup time for switching the user interaction device 202 to the passive wakeup state. The configuring device 204 compares the estimated contextual probability with the pre-defined threshold value. On determining that the estimated contextual probability is not above the pre-defined threshold value, the configuring device 204 switches the user interaction device 202 to the sleep state. On determining that the contextual probability is above the pre-defined threshold, the configuring device 204 estimates the delta wakeup time duration for switching the user interaction device 202 to the passive wakeup state. In the passive wakeup state, the user interaction device 202 continues to be active in the background state for the delta wakeup time duration, listens to the user commands and provides the responses to the user 208 without any trigger word.”)
As per claim 13, it is the device variant of claim 1 and is therefore rejected under the same rationale. (Tukka figure 7C: processor, memory.)
As per claim 15, it is the device variant of claim 3 and is therefore rejected under the same rationale.
As per claim 20, it is the non-transitory computer readable variant of claim 1 and is therefore rejected under the same rationale. (Tukka [0073]: CRM.)
Claim(s) 2, 4 – 10, 14 and 16 – 18 is/are rejected under 35 U.S.C. 103 as being unpatentable over the combination of Tukka, Bastide and Patton, and further in view of Jaureguiberry (USPAT 11294936).
As per claim 2, the combination of Tukka, Bastide and Patton did not teach:
The method of claim 1, wherein the first proxemic PDF is defined to be a Gaussian distribution having a mean based on the sensed location of the user, and wherein a standard deviation of the first proxemic PDF is adjusted based on at least one of: an estimated arm length of the user or changes in the sensed location of the user.
However, Jaureguiberry teaches:
The method of claim 1, wherein the first proxemic PDF is defined to be a Gaussian distribution having a mean based on the sensed location of the user, and wherein a standard deviation of the first proxemic PDF is adjusted based on at least one of: an estimated arm length of the user or changes in the sensed location of the user. (Jaureguiberry col 11, line 56 – col 12, line 29.)
It would have been obvious for one of ordinary skill in the art before the effective filing date of the claimed invention to incorporate the teaching of Jaureguiberry into that of Tukka, Bastide and Patton in order to the first proxemic PDF is defined to be a Gaussian distribution having a mean based on the sensed location of the user, and wherein a standard deviation of the first proxemic PDF is adjusted based on at least one of: an estimated arm length of the user or changes in the sensed location of the user. Bastide [0027] teaches using signal strength to determine proximity between users. Jaureguiberry col 11, line 56 – col 12, line 29 has shown that the claimed Gaussian distribution with standard deviation is a commonly known and used methods to determine location and proximity, thus applicants have merely claimed the combination of known parts in the field to achieve predictable results of accurately calculate proximity and is therefore rejected under 35 USC 103.
As per claim 4, the combination of Tukka, Bastide and Patton did not teach:
The method of claim 3, wherein the first proxemic PDF is a mixed Gaussian distribution and each of the two or more constituent PDFs is a respective Gaussian distribution.
However, Jaureguiberry teaches:
The method of claim 3, wherein the first proxemic PDF is a mixed Gaussian distribution and each of the two or more constituent PDFs is a respective Gaussian distribution. (Jaureguiberry col 11, line 56 – col 12, line 29.)
It would have been obvious for one of ordinary skill in the art before the effective filing date of the claimed invention to incorporate the teaching of Jaureguiberry into that of Tukka, Bastide and Patton in order to have the first proxemic PDF is a mixed Gaussian distribution and each of the two or more constituent PDFs is a respective Gaussian distribution. Tukka [0060] teaches “the contextual probability estimation unit 308 can be configured to estimate the contextual probability of initiating the voice interaction by the at least one user 208a with the at least one user interaction device (202a) of the plurality of user interaction devices 202a-202n.”, Bastide [0027] – [0028] teaches probability can be established for each engaged user regarding their odds of interaction with a content, such combination would enhance the overall appeals of all references by allowing more accurate prediction of chances of interaction based on multiple factors, and is therefore rejected under 35 USC 103.
As per claim 5, the combination of Tukka, Bastide and Patton did not teach:
The method of claim 1, wherein the at least one other proxemic PDF represents likelihood of interaction with the at least one other human, wherein the at least one other proxemic PDF is defined to be a Gaussian distribution having a mean based on the sensed location of the at least one other human, and wherein a standard deviation of the at least one other proxemic PDF is adjusted based on at least one of: an estimated arm length of the at least one other human or changes in the sensed location of the at least one other human.
However, Jaureguiberry teaches:
The method of claim 1, wherein the at least one other proxemic PDF represents likelihood of interaction with the at least one other human, wherein the at least one other proxemic PDF is defined to be a Gaussian distribution having a mean based on the sensed location of the at least one other human, and wherein a standard deviation of the at least one other proxemic PDF is adjusted based on at least one of: an estimated arm length of the at least one other human or changes in the sensed location of the at least one other human. (Jaureguiberry col 11, line 56 – col 12, line 29.)
It would have been obvious for one of ordinary skill in the art before the effective filing date of the claimed invention to incorporate the teaching of Jaureguiberry into that of Tukka, Bastide and Patton in order to have the at least one other proxemic PDF represents likelihood of interaction with the at least one other human, wherein the at least one other proxemic PDF is defined to be a Gaussian distribution having a mean based on the sensed location of the at least one other human, and wherein a standard deviation of the at least one other proxemic PDF is adjusted based on at least one of: an estimated arm length of the at least one other human or changes in the sensed location of the at least one other human. Tukka [0060] teaches “the contextual probability estimation unit 308 can be configured to estimate the contextual probability of initiating the voice interaction by the at least one user 208a with the at least one user interaction device (202a) of the plurality of user interaction devices 202a-202n.”, Bastide [0027] – [0028] teaches probability can be established for each engaged user regarding their odds of interaction with a content, such combination would enhance the overall appeals of all references by allowing more accurate prediction of chances of interaction based on multiple factors, and is therefore rejected under 35 USC 103.
As per claim 6, the combination of Tukka, Bastide, Patton and Jaureguiberry further teach:
The method of claim 5, wherein the entropy metric represents likelihood of a particular type of interaction with the at least one other human, and the electronic device is transitioned to the engaged mode dependent on the type of interaction. (Bastide [0031]: “A probability of a negative interaction between the roaming and engaged users is determined at step 230. An interaction is considered to be a negative interaction when the engaged user is likely to be startled by the interaction, thereby losing focus in performing the task.”; [0034]: “When the probability of a negative interaction exceeds the negative interaction threshold indicating that a negative interaction is likely, the roaming user is informed of, or permitted to negotiate, about the attempted interaction using a client device 114 at step 245. For example, the roaming user may be notified on client device 114 to avoid interacting with the engaged user via message exchange module 124 of server system 110.”)
As per claim 7, the combination of Tukka, Bastide, Patton and Jaureguiberry further teach:
The method of claim 5, wherein the sensed data represents sensed locations of a plurality of other humans, wherein there is a respective plurality of other proxemic PDFs representing likelihood of interaction with the respective plurality of other humans, and wherein the entropy metric is generated based on overlaps between the first proxemic PDF and each other proxemic PDF. (Bastide [0041]: “When one or more roaming users are identified as approaching the engaged user, the probability of interaction between the roaming and engaged users is determined at step 315.”; [0042]: “The determined probability of the interaction for each of the one or more roaming users is compared to an interaction threshold at step 320 to determine a likelihood of interaction between each roaming user and the engaged user.”; Patton [0116]: overlap.)
As per claim 8, the combination of Tukka, Bastide, Patton and Jaureguiberry further teach:
The method of claim 7, wherein, in response to the entropy metric indicating significant overlaps between the first proxemic PDF and two or more other proxemic PDFs corresponding to two or more other humans, the electronic device is transitioned to the engaged mode wherein the electronic device is controlled to interact with two or more other devices associated with the two or more other humans. (Tukka [0071].)
As per claim 9, the combination of Tukka, Bastide and Patton further teach:
The method of claim 1, wherein the at least one other proxemic PDF represents likelihood of interaction with the at least one other device, (Tukka [0077]: “estimating, by the configuring device 204, the contextual probability of the user initiating the interaction with the user interaction device 202. On determining the occurrence of the at least one of the first event and the successive events, the configuring device 204 estimates the contextual probability. The configuring device 204 estimates the contextual probability based on the occurrence of the events and the context. The context can be determined using the context parameters such as, but not limited to, the user context, the user PLM data, the device context, the history of voice interactions, and history of sequence of events and so on… Using the correlation, the first event, and the successive events with the context, the configuring device 204 can predict with a confidence value that the likelihood of user initiating the voice conversation with the user interaction device 202. The confidence value indicates the contextual probability of initiating the voice conversation with the user interaction device 202.”)
Tukka, Bastide and Patton did not teach:
and the at least one other proxemic PDF is defined to be a Gaussian distribution having a mean based on the sensed location of the at least one other device
However, Jaureguiberry teaches:
and the at least one other proxemic PDF is defined to be a Gaussian distribution having a mean based on the sensed location of the at least one other device. (Jaureguiberry col 11, line 56 – col 12, line 29.)
It would have been obvious for one of ordinary skill in the art before the effective filing date of the claimed invention to incorporate the teaching of Jaureguiberry into that of Tukka, Bastide and Patton in order to have the at least one other proxemic PDF is defined to be a Gaussian distribution having a mean based on the sensed location of the at least one other device. Bastide [0027] teaches using signal strength to determine proximity between users. Jaureguiberry col 11, line 56 – col 12, line 29 has shown that the claimed Gaussian distribution with standard deviation is a commonly known and used methods to determine location and proximity, thus applicants have merely claimed the combination of known parts in the field to achieve predictable results of accurately calculate proximity and is therefore rejected under 35 USC 103.
As per claim 10, the combination of Tukka, Bastide, Patton and Jaureguiberry further teach:
The method of claim 9, wherein the sensed data represents sensed locations of a plurality of other devices, wherein there is a respective plurality of other proxemic PDFs representing likelihood of interaction with the respective plurality of other devices, and wherein a respective plurality of entropy metrics is generated to represent likelihood of interaction with the respective plurality of other devices. (Tukka [0070])
As per claim 14, it is the device variant of claim 2 and is therefore rejected under the same rationale.
As per claim 16, it is the device variant of claim 5 and is therefore rejected under the same rationale.
As per claim 17, it is the device variant of claim 6 and is therefore rejected under the same rationale.
As per claim 18, it is the device variant of claim 9 and is therefore rejected under the same rationale.
Claim(s) 11 and 19 is/are rejected under 35 U.S.C. 103 as being unpatentable over the combination of Tukka, Bastide and Patton, and further in view of Hughes et al (US 20210406215, hereinafter Hughes).
As per claim 11, the combination of Tukka, Bastide and Patton did not teach:
The method of claim 1, wherein the entropy metric is represented in binary bits.
However, Hughes teaches:
The method of claim 1, wherein the entropy metric is represented in binary bits. (Hughes [0175])
It would have been obvious for one of ordinary skill in the art before the effective filing date of the claimed invention to incorporate the teaching of Hughes into that of Tukka, Bastide and Patton in order to have the entropy metric represented in binary bits. Hughes [0175] teaches binary bits are commonly used storage marker for complex records, one of ordinary skill in the art can easily see that using binary bits can allow easy tracking of specific user to device combination that would likely result in interactions, such combination would enhance the overall appeals of all references by keeping the record in a database like format for easy and accurate record keeping and is therefore rejected under 35 USC 103.
As per claim 19, it is the device variant of claim 11 and is therefore rejected under the same rationale.
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure.
Bulut et al (US 20190007354) teaches “receiving data corresponding to an interaction with a user; based on the received data, predicting a moment in time when a state of the user is likely to change; and causing a change in one or a combination of message function characteristics or data collection function characteristics at the moment in time.”;
Zhong et al (USPAT 10051600) teaches “determining a likelihood that a user is present in physical proximity to one or more computing devices based on factors such as detecting the user's voice, receiving beaconing signals from a user's mobile device, location information sent from other devices, input received via a camera, and/or other input at various devices that are geographically dispersed. Based on user presence information, which may include interaction timestamps, a system may determine confidence levels regarding which of a number of computing devices are in physical proximity of the user at a current time. The confidence scores may be used to determine whether to broadcast a meeting notification or other notification for the user to a given device for audible or visual presentation by the device.”;
Dharawat et al (US 20160061600) teaches “enable a device to detect, via environmental variances, proximity of a user and then trigger functions of the device based on the user proximity. The device may also determine if conditions of an environment caused false detection of an environmental variance, in which case, the environmental variance may be disregarded to prevent false triggering of the device functions.”
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/CHARLES M SWIFT/Primary Examiner, Art Unit 2196