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 .
Response to Amendment and Response Under 37 CFR 1.114 (RCE)
Applicant filed an amendment and response Under 37 CFR 1.114 (RCE) on December 18, 2025. Applicant amended claim 1. Claims 1-3, 6, 8-13 and 16-18 remain pending in the application.
Claim Rejections - 35 USC § 103
In the event the determination of the status of the application as subject to AIA 35 U.S.C. 102 and 103 (or as subject to pre-AIA 35 U.S.C. 102 and 103) is incorrect, any correction of the statutory basis (i.e., changing from AIA to pre-AIA ) for the rejection will not be considered a new ground of rejection if the prior art relied upon, and the rationale supporting the rejection, would be the same under either status.
The following is a quotation of 35 U.S.C. 103 which forms the basis for all obviousness rejections set forth in this Office action:
A patent for a claimed invention may not be obtained, notwithstanding that the claimed invention is not identically disclosed as set forth in section 102, if the differences between the claimed invention and the prior art are such that the claimed invention as a whole would have been obvious before the effective filing date of the claimed invention to a person having ordinary skill in the art to which the claimed invention pertains. Patentability shall not be negated by the manner in which the invention was made.
The factual inquiries for establishing a background for determining obviousness under 35 U.S.C. 103 are summarized as follows:
1. Determining the scope and contents of the prior art.
2. Ascertaining the differences between the prior art and the claims at issue.
3. Resolving the level of ordinary skill in the pertinent art.
4. Considering objective evidence present in the application indicating obviousness or nonobviousness.
Claims 1-3, 6, 8-13, and 17 are rejected under 35 U.S.C. 103 as being unpatentable over US 20210365815 A1 (“Bonutti”) in view of CN 107851356 A (“Pradeep”).
In regards to claim 1, Bonutti discloses the following limitations with the exception of the underlined limitation.
An artificial intelligence psychological stress detection device, comprising ([0004], “an artificial intelligence (AI) system”; [0219], “Method 2000 has broad application to … psychological studies”; [0065], “communicate to … healthcare provider how much … stress … is appropriate for an individual …”; [0113], “AI system 104 detects and compiles patient … patterns”):
a mental sensor chip, configured to sense a user’s physiological characteristic(s) and/or behavior characteristic(s) and/or environmental characteristic(s) ([0051], “AI system 104 is configured to implement … artificial intelligence … that … personalize … prevention of … mental impairments of the patient”; [0047], “system 100 includes one or more patient monitor sensors”; [0002], “methods and systems for … user activities”; [0007], “to determine a subject’s … physiological response”; [0070], “add … rules based on patient behaviors”; [0062], “people are exposed to different environmental conditions”);
a communication module, configured to communicate with a mobile device ([0048], “achieved via … communications networks … facilitating the exchange of data among various components of AI system 100”; [0155], “done … through a mobile device”) ;
and a microcontroller unit, connected to the mental sensor chip and the communication module, and configured to send the user’s physiological characteristic(s) and/or behavior characteristic(s) and/or environmental characteristic(s) obtained from the mental sensor chip to the mobile device via the communication module ([0142], “microcontroller may keep the collected information … locally … or may upload it to a centralized server”);
wherein the mental sensor chip includes a light sensor ([0172], “the image … device … comprises a light source … at which point an image sensor … captures an image”; Examiner notes that image sensors are designed to detect light.), wherein the light sensor is an environmental light sensor, which includes an interference filter (Examiner notes that image sensors can use interference filters.) bank deposited on a silicon component and composed of a plurality of interference filter sheets arranged in order ([0148], “equipped with sensors to look for … obstacles”, Examiner notes that obstacles can cause interference), so that the artificial intelligence psychological stress detection device forms a spectrometer or an illuminometer with a miniaturized volume ([0188], “the image creation system … comprises a computer … that sends direction to a controller … The controller … controls a visible light source … which sends light … the controller … is able to modify the focal point, energy, wavelength, activation frequency, and intensity of the light” Examiner notes that a spectrometer measures the properties of light and that an image creation system can measure the properties of light with high precision.);
wherein the environmental light sensor is configured to collect a plurality bands of light covering from violet light to infrared light, wherein the environmental light sensor comprises multiple chips, and each of the multiple chips is used to collect or receive light information of some of the plurality bands of light ([0173], “visible spectrum … source of near infrared light, ultraviolet light” Examiner notes that a sensor inherently includes), wherein the interference filter bank is configured to filter out specific bands of light from the plurality bands of light, and remain at least one band of light to the silicon component ([0168], “The image processor … is configured … to process the image data and generate … outputs based on the image data.” Examiner notes that an image processor can filter out light.);
wherein the bands of the light collected by the environmental light sensor are mapped to a prediction result by a machine learning module, and the prediction result is associated with a physical field where the artificial intelligence psychological stress detection device locates ([0004], “AI system is configured to implement … machine learning … AI system is configured to receive and analyze the monitored physical properties”);
wherein the artificial intelligence psychological stress detection device is configured to issue a notification to the user of a need of exposure to real sun light when the user is found locating at a field where specific spectral irradiances of green light, red light, and/or infrared light are lower than threshold values.
Pradeep discloses
wherein the artificial intelligence psychological stress detection device is configured to issue a notification to the user of a need of exposure to real sun light when the user is found locating at a field where specific spectral irradiances of green light, red light, and/or infrared light are lower than threshold values ((21), “An infant health condition monitoring system, comprising ... the central processor further is configured to operate in the presence of a health condition, notification to infant caregiver”).
Bonutti and Pradeep combined are considered analogous to the claimed invention because they are in the field of monitoring devices. Therefore, it would have been obvious to a person of ordinary skill in the art before the effective filing date of the applicant’s invention for an artificial intelligence psychological stress detection device, comprising: a mental sensor chip, configured to sense a user’s physiological characteristic(s) and/or behavior characteristic(s) and/or environmental characteristic(s); a communication module, configured to communicate with a mobile device; and a microcontroller unit, connected to the mental sensor chip and the communication module, and configured to send the user’s physiological characteristic(s) and/or behavior characteristic(s) and/or environmental characteristic(s) obtained from the mental sensor chip to the mobile device via the communication module; wherein the mental sensor chip includes a light sensor, wherein the light sensor is an environmental light sensor, which includes an interference filter bank deposited on a silicon component and composed of a plurality of interference filter sheets arranged in order, so that the artificial intelligence psychological stress detection device forms a spectrometer or an illuminometer with a miniaturized volume; wherein the environmental light sensor is configured to collect a plurality bands of light covering from violet light to infrared light, wherein the environmental light sensor comprises multiple chips, and each of the multiple chips is used to collect or receive light information of some of the plurality bands of light, wherein the interference filter bank is configured to filter out specific bands of light from the plurality bands of light, and remain at least one band of light to the silicon component; wherein the bands of the light collected by the environmental light sensor are mapped to a prediction result by a machine learning module, and the prediction result is associated with a physical field where the artificial intelligence psychological stress detection device locates, as disclosed by Bonutti, wherein the artificial intelligence psychological stress detection device is configured to issue a notification to the user of a need of exposure to real sun light when the user is found locating at a field where specific spectral irradiances of green light, red light, and/or infrared light are lower than threshold values, as disclosed by Pradeep, to provide a central processor and notification for an infant health condition monitoring system. One skilled in the art would understand and recognize the value of the addition of a central processor and notification to improve the effectiveness of an infant health condition monitoring system.
In regards to claim 2, Bonutti discloses
wherein the user's physiological characteristic(s) and/or behavior characteristic(s) and/or environmental characteristic(s) include heartbeat, respiration, intensity or spectrum of light illumination, skin impedance, sleep, or activity ([0049], “patient monitor sensors 102 can … record and/or transmit patient metrics (e.g., heartbeat, quality of sleep, movements, sleep patterns, skin resistance”; [0173], “Additional exemplary sensors … to collect data relative to … body temperature, … heart rate, … respiration”; [0174], “activity trackers … light within the spectrum … light source 1430 to illuminate … intensity of the light”).
In regards to claim 3, Bonutti discloses
wherein the mental sensor chip further includes a gyroscope, an accelerometer, a gravity sensor, a heart rate sensor, and/or a temperature sensor ([0049], “Exemplary sensors include … accelerometers, goniometers, and … custom tracking devices with the ability to record and/or transmit patient metrics”; [0061], “Additional exemplary sensors … to collect data relative to … body temperature … aspects of system 100 … can direct a robotic medical device … minimally invasive approaches, such as by magnetic guidance”, which mimics a magnetometer that can be used with an accelerometer to replace a gyroscope (device used to maintain orientation).
In regards to claim 6, Bonutti discloses
wherein the bands of the light cover wavelengths from 410nm to 940nm ([0177], “The retinal scanner … includes an infrared light source 1442 that sends a low energy, narrow beam of light through an optical fiber 1444”). Examiner determined that infrared wavelength ranges from 710nm to 1mm. Although wavelengths less than 710nm do not fall within the infrared range, wavelengths from 710nm to 940nm do fall within the infrared range. “[A] prior art reference that discloses a range encompassing a somewhat narrower claimed range is sufficient to establish a prima facie case of obviousness." In re Peterson, 315 F.3d 1325, 1330, 65 USPQ2d 1379, 1382-83 (Fed. Cir. 2003). See also In re Harris, 409 F.3d 1339, 74 USPQ2d 1951 (Fed. Cir. 2005) (claimed alloy held obvious over prior art alloy that taught ranges of weight percentages overlapping, and in most instances completely encompassing, claimed ranges; furthermore, narrower ranges taught by reference overlapped all but one range in claimed invention).
In regards to claim 8, Bonutti discloses
wherein the machine learning module is constructed on the mobile device itself, or the machine learning module is constructed on a cloud server communicating with the mobile device ([0113], “data repository (e.g., database, cloud service …)”.
In regards to claim 9, Bonutti discloses
wherein the machine learning module is formed by a Recurrent Neural Network (RNN), a Support Vector Machine (SVM), a Deep Neural Network (DNN), and/or a Fuzzy Neural Network (FNN) ([0086], “AI system 104 utilizes other known AI techniques such as neural networks”; [0004], “AI system is configured to implement … machine learning”, where the support vector machine is a type of algorithm used in machine learning).
In regards to claim 10, Bonutti discloses
wherein the RNN extracts personalized characteristics from the user's physiological characteristic(s) and/or behavior characteristic(s) and/or environmental characteristic(s), and the SVM reclassifies the personalized characteristics ([0004], “parameters optimized or personalized to the user”).
In regards to claim 11, Bonutti discloses
wherein the FNN is used to derive a care solution from several possible solutions after detection results are inputted into the FNN ([0080], “intelligent systems can be used to optimize systems and methods … to provide better care”; [0086], “algorithms … produce several solutions to a given problem”).
In regards to claim 12, Bonutti discloses
wherein the FNN is configured to utilize fuzzy decision making ([0086], “AI system 104 utilizes other known AI techniques such as neural networks”, [0055], “global expert system 112 is configured to emulate decision-making abilities”).
In regards to claim 13, Bonutti does not disclose wherein the microcontroller unit is configured to issue a notification when light of a specific wavelength obtained by an environmental light sensor of the mental sensor chip is lower than a spectral irradiance threshold value.
Pradeep discloses
wherein the microcontroller unit is configured to issue a notification when light of a specific wavelength obtained by an environmental light sensor of the mental sensor chip is lower than a spectral irradiance threshold value ((47) “environment adaptability model comprises light intensity threshold, wherein more than the light intensity threshold results in determining the environmental condition is not suitable” Examiner notes that Pradeep’s device discloses a central processor to transmit a notification. (21))
It would have been obvious to a person of ordinary skill in the art before the effective filing date of the applicant’s invention to modify the microcontroller of Bonutti to issue a notification when light of a specific wavelength obtained by an environmental light sensor of the mental sensor chip is lower than a spectral irradiance threshold value, as disclosed by Pradeep, in order to transmit a notification based on a threshold value.
Bonutti and Pradeep combined are considered analogous to the claimed invention because they are in the field of monitoring devices. Therefore, it would have been obvious to a person of ordinary skill in the art before the effective filing date of the applicant’s invention for an artificial intelligence psychological stress detection device, comprising: a mental sensor chip, configured to sense a user’s physiological characteristic(s) and/or behavior characteristic(s) and/or environmental characteristic(s); a communication module, configured to communicate with a mobile device; and a microcontroller unit, connected to the mental sensor chip and the communication module, and configured to send the user’s physiological characteristic(s) and/or behavior characteristic(s) and/or environmental characteristic(s) obtained from the mental sensor chip to the mobile device via the communication module; wherein the mental sensor chip includes a light sensor, wherein the light sensor is an environmental light sensor, which includes an interference filter bank deposited on a silicon component and composed of a plurality of interference filter sheets arranged in order, so that the artificial intelligence psychological stress detection device forms a spectrometer or an illuminometer with a miniaturized volume; wherein the environmental light sensor is configured to collect a plurality bands of light covering from violet light to infrared light, wherein the environmental light sensor comprises multiple chips, and each of the multiple chips is used to collect or receive light information of some of the plurality bands of light, wherein the interference filter bank is configured to filter out specific bands of light from the plurality bands of light, and remain at least one band of light to the silicon component; wherein the bands of the light collected by the environmental light sensor are mapped to a prediction result by a machine learning module, and the prediction result is associated with a physical field where the artificial intelligence psychological stress detection device locates, as disclosed by Bonutti, wherein the artificial intelligence psychological stress detection device is configured to issue a notification to the user of a need of exposure to real sun light when the user is found locating at a field where specific spectral irradiances of green light, red light, and/or infrared light are lower than threshold values, wherein the microcontroller unit is configured to issue a notification when light of a specific wavelength obtained by an environmental light sensor of the mental sensor chip is lower than a spectral irradiance threshold value, as disclosed by Pradeep, to provide an environment adaptability model and a light intensity threshold for an infant health condition monitoring system. One skilled in the art would understand and recognize the value of the addition of an environment adaptability model and a light intensity threshold to improve the effectiveness of an infant health condition monitoring system.
In regards to claim 17, Bonutti discloses
wherein the artificial intelligence psychological stress detection device is a portable device or a wearable device ([0098], “alerting algorithm 200 can be executed on a portable computing device”; [0049], “Exemplary sensors include … wireless-enabled wearable devices”).
Claim 16 is rejected under 35 U.S.C. 103 as being unpatentable over Bonutti in view of U. S. Pub. 20210345926 (“Son”).
In regards to claim 16, Bonutti discloses wherein the artificial intelligence psychological stress detection device is configured to read a pressure index and issue a notification, ([0234], “such an embodiment could, … estimate when and for how long the user should sleep and how dramatically it will affect the user's mood... Anything else that would regularly affect mood … could be recorded, measured, or both” (where in the current application’s specification [0088] ‘prediction result may be converted into a “mood index” or a “pressure index”’)).
Bonutti does not disclose and the notification includes a personalized adjustment strategy when the pressure index exceeds a threshold value.
Son discloses and the notification includes a personalized adjustment strategy when the pressure index exceeds a threshold value ([0035], “control unit 130 classifies and determines … psychological health state of the user … and controls … information to be displayed through the light emitting unit 140”; [0036], control unit 130 stores the sensor values … determines psychological health state of the user … based on the preset set value through a calculation process”).
Bonutti and Son combined are considered analogous to the claimed invention because they are in the field of monitoring devices and systems. Therefore, it would have been obvious to a person of ordinary skill in the art before the effective filing date of the applicant’s invention for an artificial intelligence psychological stress detection device, comprising: a mental sensor chip, configured to sense a user’s physiological characteristic(s) and/or behavior characteristic(s) and/or environmental characteristic(s); a communication module, configured to communicate with a mobile device; and a microcontroller unit, connected to the mental sensor chip and the communication module, and configured to send the user’s physiological characteristic(s) and/or behavior characteristic(s) and/or environmental characteristic(s) obtained from the mental sensor chip to the mobile device via the communication module; wherein the mental sensor chip includes a light sensor, wherein the light sensor is an environmental light sensor, which includes an interference filter bank deposited on a silicon component and composed of a plurality of interference filter sheets arranged in order, so that the artificial intelligence psychological stress detection device forms a spectrometer or an illuminometer with a miniaturized volume; wherein the environmental light sensor is configured to collect a plurality bands of light covering from violet light to infrared light, wherein the environmental light sensor comprises multiple chips, and each of the multiple chips is used to collect or receive light information of some of the plurality bands of light, wherein the interference filter bank is configured to filter out specific bands of light from the plurality bands of light, and remain at least one band of light to the silicon component; wherein the bands of the light collected by the environmental light sensor are mapped to a prediction result by a machine learning module, and the prediction result is associated with a physical field where the artificial intelligence psychological stress detection device locates, as disclosed by Bonutti, and the notification includes a personalized adjustment strategy when the pressure index exceeds a threshold value, as disclosed by Son, to provide a control unit for a psychological health monitoring system. One skilled in the art would understand and recognize the value of the addition of a control unit to improve the effectiveness of a psychological health monitoring system.
Claim 18 is rejected under 35 U.S.C. 103 as being unpatentable over Bonutti in view of EVENSON, KR et al. Systematic Review of the Validity and Reliability of Consumer-Wearable Activity Trackers, International Journal of Behavioral Nutrition and Physical Activity, Vol 12, 18 December 2015, pp. 159 - <DOI 10.1186/s12966-015-0314-1> (“Evenson”) and Manifest, The. 56% of People Own at Least One Wearable, as Google Competes for Market Share, PR Newswire: Press Release Distribution, Targeting, Monitoring and Marketing, Cision PR Newswire, 14 November 2019 [online], [retrieved on 2025-03-31]. Retrieved from the Internet <URL: http://www.prnewswire.com/news-releases/56-of-people-own-at-least-one-wearable-as-google-competes-for-market-share-300958174.html> (“The Manifest”).
In regards to claim 18, Bonutti discloses
wherein the artificial intelligence psychological stress detection device ([0004], “an artificial intelligence (AI) system”; [0219], “Method 2000 has broad application to … psychological studies”; [0065], “communicate to … healthcare provider how much … stress … is appropriate for an individual …”; [0113], “AI system 104 detects and compiles patient … patterns”).
Bonutti does not disclose has a volume less than 32.5 cm3, a weight less than 28g.
Evenson discloses
has a volume less than 32.5 cm3, a weight less than 28g, (“Table 1 … Tracker Fitbit Zip … Size (cm) 3.6(h) × 2.9(w) × 1.0(d) … Weight (g) 8”) (Examiner notes that the detection device is associated with Tracker Fitbit Zip and that the volume and weight of Tracker Fitbit Zip is 10.44 cm3 and 8g, respectively). Evenson optimizes the volume and weight which lie inside the range for the claimed invention. Therefore, in the case where the claimed ranges “overlap or lie inside ranges disclosed by the prior art”, a prima facie case of obviousness exists. In re Geisler, 116 F.3d 1465, 1469-71, 43 USPQ2d 1362, 1365-66 (Fed. Cir. 1997) (Claim reciting thickness of a protective layer as falling within a range of “50 to 100 Angstroms” considered prima facie obvious in view of prior art reference teaching that “for suitable protection, the thickness of the protective layer should be not less than about 10 nm [i.e., 100 Angstroms].” The court stated that “by stating that ‘suitable protection’ is provided if the protective layer is ‘about’ 100 Angstroms thick, [the prior art reference] directly teaches the use of a thickness within [applicant’s] claimed range.”). See MPEP § 2144.05.
Thus, it would have been obvious to a person of ordinary skill in the art before the effective filing date of the applicant’s invention for a detection device to modify Bonutti with a volume less than 32.5 cm3 and a weight less than 28g, as disclosed by Evenson. In re Fine, 837 F.2d 1071, 5 USPQ2d 1596 (Fed. Cir. 1988); In re Jones, 958 F.2d 347, 21 USPQ2d 1941 (Fed. Cir. 1992); see also In re Kotzab, 217 F.3d 1365, 1370, 55 USPQ2d 1313, 1317 (Fed. Cir. 2000). (see MPEP 2144(I)).
Bonutti does not disclose and/or a stand-by time more than 37 hours.
The Manifest discloses
and/or a stand-by time more than 37 hours (“Fitbit, however, has extended the battery life of its smart watches, Versa and Ionic, to four to five days.”).
Thus, it would have been obvious to a person of ordinary skill in the art before the effective filing date of the applicant’s invention for a detection device to modify the battery life (stand-by time) to greater than 37 hours (which lies inside the range for the claimed invention), as disclosed by The Manifest. In re Fine, 837 F.2d 1071, 5 USPQ2d 1596 (Fed. Cir. 1988); In re Jones, 958 F.2d 347, 21 USPQ2d 1941 (Fed. Cir. 1992); see also In re Kotzab, 217 F.3d 1365, 1370, 55 USPQ2d 1313, 1317 (Fed. Cir. 2000). (see MPEP 2144(I)). Therefore, in the case where the claimed ranges “overlap or lie inside ranges disclosed by the prior art”, a prima facie case of obviousness exists. In re Geisler, 116 F.3d 1465, 1469-71, 43 USPQ2d 1362, 1365-66 (Fed. Cir. 1997) (Claim reciting thickness of a protective layer as falling within a range of “50 to 100 Angstroms” considered prima facie obvious in view of prior art reference teaching that “for suitable protection, the thickness of the protective layer should be not less than about 10 nm [i.e., 100 Angstroms].” The court stated that “by stating that ‘suitable protection’ is provided if the protective layer is ‘about’ 100 Angstroms thick, [the prior art reference] directly teaches the use of a thickness within [applicant’s] claimed range.”). See MPEP § 2144.05.
Response to Arguments
Applicant's arguments filed December 18, 2025 have been fully considered but they are not persuasive. Claims 1-3, 6, 8-13 and 16-18 remain pending in the application. With respect to claim 1, “Applicant submits that each of the cited references fails to disclose or suggest that ‘the artificial intelligence psychological stress detection device forms a spectrometer or an illuminometer with a miniaturized volume’ as claimed, such that the references neither anticipate nor form a prima facie case of obviousness of claim 1.” (See AMENDMENT AND RESPONSE UNDER 37 CFR 1.114 (RCE), REMARKS, Rejection of claims 1-3, 6, 8-13 and 16-18 under 35 U.S.C. § 102, 103, page 8, paragraph 1), “Applicant submits that each of the cited references fails to disclose or suggest that ‘the interference filter bank is configured to filter out specific bands of light from the plurality bands of light, and remain at least one band of light to the silicon component’ as claimed, such that the references neither anticipate nor form a prima facie case of obviousness of claim 1.” (See AMENDMENT AND RESPONSE UNDER 37 CFR 1.114 (RCE), REMARKS, Rejection of claims 1-3, 6, 8-13 and 16-18 under 35 U.S.C. § 102, 103, page 9, paragraph 1), and “Applicant submits that each of the cited references fails to disclose or suggest that ‘the artificial intelligence psychological stress detection device is configured to issue a notification to the user of a need of exposure to real sun light when the user is found locating at a field where specific spectral irradiances of green light, red light, and/or infrared light are lower than threshold values’ as claimed, such that the references neither anticipate nor form a prima facie case of obviousness of claim 1.” (See AMENDMENT AND RESPONSE UNDER 37 CFR 1.114 (RCE), REMARKS, Rejection of claims 1-3, 6, 8-13 and 16-18 under 35 U.S.C. § 102, 103, page 10, paragraph 2). Examiner acknowledges Applicant’s remarks. However, Examiner notes in the 35 USC § 103 rejection of claim 1 Bonutti discloses an artificial intelligence psychological stress detection device, comprising ([0004], “an artificial intelligence (AI) system”; [0219], “Method 2000 has broad application to … psychological studies”; [0065], “communicate to … healthcare provider how much … stress … is appropriate for an individual …”; [0113], “AI system 104 detects and compiles patient … patterns”): a mental sensor chip, configured to sense a user’s physiological characteristic(s) and/or behavior characteristic(s) and/or environmental characteristic(s) ([0051], “AI system 104 is configured to implement … artificial intelligence … that … personalize … prevention of … mental impairments of the patient”; [0047], “system 100 includes one or more patient monitor sensors”; [0002], “methods and systems for … user activities”; [0007], “to determine a subject’s … physiological response”; [0070], “add … rules based on patient behaviors”; [0062], “people are exposed to different environmental conditions”); a communication module, configured to communicate with a mobile device ([0048], “achieved via … communications networks … facilitating the exchange of data among various components of AI system 100”; [0155], “done … through a mobile device”) ; and a microcontroller unit, connected to the mental sensor chip and the communication module, and configured to send the user’s physiological characteristic(s) and/or behavior characteristic(s) and/or environmental characteristic(s) obtained from the mental sensor chip to the mobile device via the communication module ([0142], “microcontroller may keep the collected information … locally … or may upload it to a centralized server”); wherein the mental sensor chip includes a light sensor ([0172], “the image … device … comprises a light source … at which point an image sensor … captures an image”; Examiner notes that image sensors are designed to detect light.), wherein the light sensor is an environmental light sensor, which includes an interference filter (Examiner notes that image sensors can use interference filters.) bank deposited on a silicon component and composed of a plurality of interference filter sheets arranged in order ([0148], “equipped with sensors to look for … obstacles”, Examiner notes that obstacles can cause interference), so that the artificial intelligence psychological stress detection device forms a spectrometer or an illuminometer with a miniaturized volume ([0188], “the image creation system … comprises a computer … that sends direction to a controller … The controller … controls a visible light source … which sends light … the controller … is able to modify the focal point, energy, wavelength, activation frequency, and intensity of the light” Examiner notes that a spectrometer measures the properties of light and that an image creation system can measure the properties of light with high precision.); wherein the environmental light sensor is configured to collect a plurality bands of light covering from violet light to infrared light, wherein the environmental light sensor comprises multiple chips, and each of the multiple chips is used to collect or receive light information of some of the plurality bands of light ([0173], “visible spectrum … source of near infrared light, ultraviolet light” Examiner notes that a sensor inherently includes), wherein the interference filter bank is configured to filter out specific bands of light from the plurality bands of light, and remain at least one band of light to the silicon component ([0168], “The image processor … is configured … to process the image data and generate … outputs based on the image data.” Examiner notes that an image processor can filter out light.); wherein the bands of the light collected by the environmental light sensor are mapped to a prediction result by a machine learning module, and the prediction result is associated with a physical field where the artificial intelligence psychological stress detection device locates ([0004], “AI system is configured to implement … machine learning … AI system is configured to receive and analyze the monitored physical properties”) and Pradeep discloses wherein the artificial intelligence psychological stress detection device is configured to issue a notification to the user of a need of exposure to real sun light when the user is found locating at a field where specific spectral irradiances of green light, red light, and/or infrared light are lower than threshold values ((21), “An infant health condition monitoring system, comprising ... the central processor further is configured to operate in the presence of a health condition, notification to infant caregiver”).
MPEP § 2111 discusses proper claim interpretation, including giving claims their
broadest reasonable interpretation (“BRI”) in light of the specification during
examination. Under BRI, the words of a claim must be given their plain meaning unless
such meaning is inconsistent with the specification, and it is improper to import claim
limitations from the specification into the claim. Applicant’s argument is not persuasive because the BRI is broader than what is argued. Therefore, the rejections of claims 1-3, 6, 8-13, and 16-18, as obvious by Bonutti in view of Pradeep, are maintained.
As to establishing a prima facie case of obviousness, upon review, the
examiner’s rejection satisfied the requirements for applying Rationale G in 2143(I)(G).
Applicant’s argument is not persuasive because the argument does not meet the
requirements of 37 C.F.R. 1.111(b), and, upon review, the rejection does make a prima
facie case using 2143(I)(G). Therefore, the rejections of claims 1-3, 6, 8-13, and 16-18, as obvious by Bonutti in view of Pradeep, are maintained.
Contact Information
Any inquiry concerning this communication or earlier communications from the
examiner should be directed to Lisa Antoine whose telephone number is
(571) 272-4252 and whose email address is lantoine@uspto.gov. The examiner can be reached Monday-Thursday, 7:30 am – 5:30 pm CT. Examiner interviews are available via telephone, in-person, and video conferencing using a USPTO supplied web-based collaboration tool. To schedule an interview, applicant is encouraged to use the USPTO Automated Interview Request (AIR) at http://www.uspto.gov/interviewpractice. If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, Xuan Thai, can be reached on (571) 272-7147. The fax phone number for the organization where this application or proceeding is assigned is (571) 273-8300.
Publication Information
Information regarding the status of published or unpublished applications may be
obtained from the Patent Center. Unpublished application information in the Patent
Center is available to registered users. To file and manage patent submissions in the
Patent Center, visit: https://patentcenter.uspto.gov. Visit
https://www.uspto.gov/patents/apply/patent-center for more information about the
Patent Center and https://www.uspto.gov/patents/docx for information about filing in
DOCX format. For additional questions, contact the Electronic Business Center (EBC)
at 866-217-9197 (toll-free). If you would like assistance from a USPTO Customer
Service Representative, call 800-786-9199 (IN USA OR CANADA) or 571-272-1000.
/LISA H ANTOINE/
Examiner, Art Unit 3715
/XUAN M THAI/Supervisory Patent Examiner, Art Unit 3715