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 § 101
35 U.S.C. 101 reads as follows:
Whoever invents or discovers any new and useful process, machine, manufacture, or composition of matter, or any new and useful improvement thereof, may obtain a patent therefor, subject to the conditions and requirements of this title.
Claim 1 and 8 and 15 are rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more.
Claim 1 and 8 and 15 are rejected under 35 U.S.C. 101 because the claimed invention is directed to a judicial exception (i.e., a law of nature, a natural phenomenon, or an abstract idea) without significantly more. Claim(s) 1 and 8 and 15 “recites” abstract ideas under the 2019 PEG:
Step 2A, Prong One
Claims recites an abstract idea in the form of a mental process, namely, collecting information about entities, obtaining information about outside activity, monitoring subsequent activity, and identifying a location with a likelihood of being hazardous by correlating the subsequent activity with the obtained information, which amounts to observation, evaluation, and judgment. Such concepts fall within the mental-process grouping of abstract ideas.
Step 2A, Prong Two
The additional elements, including the recited electronic device and performance of the steps in an interior space, do not integrate the abstract idea into a practical application. The claim does not recite any particular technical manner for detecting, monitoring, or correlating beyond generic information gathering and analysis. It does not improve a computer, sensor, or other technology, and it does not recite a particular machine implementation in a meaningful way. Rather, the electronic device merely collects information and applies the correlation to identify a hazardous location.
Step 2B
The claim does not include significantly more than the abstract idea itself. The additional elements amount to generic device implementation of data collection, monitoring, and analysis functions, i.e., well-understood, routine, conventional computer functions. Accordingly, claim 1, 8 and 15 is directed towards an abstract idea.
Claim Objections
Claim 20 is objected to because of the following informalities: This claim appears to omit the noun after “at least one”. Based on claim 6 and 17, it appears that claim 20 should read “the identifying of the at least one location comprises”. Appropriate correction is required.
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.
Claim(s) 1, 3-6, 8, 10-13, 15 and 17-20 is/are rejected under 35 U.S.C. 103 as being unpatentable over De Luca et al (US 2012/0150333) in view of Kuhnreichi (US 2013/0144527).
Per claim 1, De Luca teaches a method, by an electronic device, for identifying potential hazardous locations, the method comprising (De Luca teaches an automated computer-based monitoring system for detecting contamination activity in a production area. De Luca para 0009, 0023):
detecting one or more entities in an interior space (para 0010 teaches detecting an individual in the production area. Para 0049 further teaches the computer system can be programmed to process the image data to identify individuals);
[obtaining information associated with at least one outside activity performed by the one or more entities];
monitoring at least one subsequent activity of the one or more entities that is performed in the interior space (para 0010 teaches monitoring subsequent activity of the detected individual in the production area, including determining whether the individual engaged in a contamination event while in the production area. 0048 teaches detect a contamination event and other actions occurring in the production area requires capturing image data over a period of time. 0055 teaches the tracking and monitoring of an individual can be carried out simultaneously with the evaluation of the individual for a contamination event); and
identifying at least one location with a likelihood of being hazardous by correlating the at least one subsequent activity with the obtained information (0014 teaches identifying location in the production area associated with contamination and identifying articles/areas within a contamination zone).
But, De Luca does not explicitly teach obtaining information associated with at least one outside activity performed by the one or more entities.
In an analogous art, Kuhnreichi in para 0021 and 0066 teaches obtaining location-history / outside-activity information for a user, including where the user was and time information associated with the user’s movement. Para 0068 further teaches correlating user location/time information with environmental pollution information to determine exposure. Para 0020 further teaches determining whether pollution is inside or outside the indoor space and detecting source location. Thus, the combination teaches identifying a location in the interior space having a likelihood of being hazardous by correlating:(1) De Luca’s monitored subsequent activity in the interior production area that gives rise to contamination risk, with (2) Kuhnreichi’ s obtained outside-activity / location-history / environmental exposure information.
Therefore, before the effective filing date of the invention, it would have been obvious to one of ordinary skill in the art to modify De Luca’s interior contamination-monitoring system to additionally use Kuhnreichi’ s user location-history and environmental exposure information in order to improve hazard assessment for interior locations by accounting not only for activity observed inside the space, but also for external exposure associated with places visited and environmental conditions encountered before entering the interior space. This combination would have predictably improved contamination-risk identification and localization because Kuhnreichi expressly teaches that location/time/environmental data can be integrated to determine accumulated exposure, while De Luca expressly teaches monitoring a person’s interior activity and determining the location in the production area associated with contamination events. Combining those teachings would have allowed the system to more accurately identify interior locations likely to be hazardous based on both prior outside exposure and subsequent interior activity, thereby improving contamination prevention and response.
Per claim 3, 10 and 17, Kuhnreichi teaches wherein the identifying of the at least one location comprises:
obtaining information related to an environmental health condition of one or more visited places based on the information associated with the at least one outside activity (Kuhnreichi teaches a personal health monitoring system using indoor and outdoor sensing devices for measuring pollution and atmospheric/environmental conditions. Kuhnreichi teaches that the sensing units may include “air pollution sensors,” “water pollution sensors,” and “atmospheric condition sensors.” (para 0016-0017). Kuhnreichi also teaches “creating a time log of the user location information” and “creating pollution data tables containing location information, time information and pollutant information.” (para 0021). Thus, Kuhnreichi teaches obtaining environmental health condition information for places visited by the user based on the user’s location history / outside activity. (para 0016-0017, 0021));
calculating an external exposure contamination quotient for the one or more entities based on the information related to the environmental health condition of the one or more visited places (Kuhnreichi teaches a “method for personal accumulated pollution exposure levels estimation,” including integrating location information, time information, and pollutant information “to conclude the accumulated exposure over a predefined period of time,” and “accumulating pollution exposure levels in terms of level per period of time.” (para 0021). This reads on calculating a contamination/exposure quantity for the user based on environmental health condition information of visited places. (para 0021)); and
calculating an external exposure level by correlating the obtained information related to the environmental health condition and the external exposure contamination quotient (Kuhnreichi teaches integrating the user’s location information with pollution information to conclude accumulated exposure, i.e., correlating the environmental condition information of visited locations with the calculated exposure quantity to determine an exposure level. (para 0021). Kuhnreichi further explains that pollution levels have a quantitative relationship to health outcomes and that the system distributes “personal exposure levels” and information relevant to the user’s actual pollution exposure. (para 0005, 0012, 0021)).
Therefore, before the effective filling date of the invention, it would have been obvious to one of ordinary skill in the art to incorporate Kuhnreichi’ s environmental-condition and accumulated-exposure analysis into De Luca’s indoor contamination-monitoring system in order to improve hazard assessment for indoor locations based on not only later indoor contamination-spreading activity, but also the user’s prior external environmental exposure. De Luca already teaches identifying interior locations/articles associated with contamination events. para 0014, 0017. Kuhnreichi teaches collecting pollution and atmospheric-condition data for locations associated with a user, and integrating location/time/pollutant information to calculate accumulated exposure levels relevant to health. para 0016-0017, 0021. Combining these teachings would have predictably improved identification of interior locations likely to be hazardous by accounting for both indoor contamination-spreading activity and prior external exposure conditions.
Per claim 4, 11 and 18, De Luca does not explicitly teach wherein the information related to the environmental health condition of the one or more visited places includes at least one of a population density of the one or more visited places, weather condition information of the one or more visited places, humidity level information of the one or more visited places, dust information, pollen, or one or more pollutants available at the one or more visited places.
Kuhnreichi teaches environmental-condition information for monitored locations, including meteorological conditions such as “temperature, humidity and the like” in the vicinity of a sensing unit. (para 0054). Kuhnreichi also teaches atmospheric condition sensors for sensing “ambient temperature, barometric pressure, relative humidity, UV radiation ... and other or additional atmospheric conditions.” (para 0032) Kuhnreichi further teaches air pollution sensors for sensing gases and particles, including “nitrogen dioxide,” “nitrogen oxide,” “carbon oxide,” “sulfur dioxide,” “ozone,” “volatile organic compounds,” and “particles by size, such as: 10-micron, 2.5 micron and nano-metric scale particles.” (para 0031) These particle measurements reasonably read on dust information, and the listed gaseous/particle measurements read on one or more pollutants available at the visited places. Kuhnreichi additionally teaches creating pollution data tables containing “location, time and pollutant” information based on geospatial and personal location data. (para 0067)
Thus, Kuhnreichi teaches the recited environmental-health-condition information including at least weather condition information, humidity level information, dust information, and one or more pollutants for the visited places. (para 0031-0033, 0054, 0067).
Therefore, before the effective filling date of the invention, it would have been obvious to one of ordinary skill in the art to use Kuhnreichi’ s specific environmental-condition inputs, such as atmospheric conditions, humidity, particle levels, and pollutant levels, in De Luca’s indoor contamination-monitoring system to improve estimation of external exposure conditions associated with places visited before entry into the interior space. Doing so would have predictably improved the system’s ability to identify indoor locations likely to be hazardous or contaminated based on both prior outside exposure conditions and later indoor contamination-spreading activity.
Per claim 5, 12 and 19, De Luca does not explicitly teach wherein the information associated with at least one outside activity includes at least one of places visited by the one or more entities or total time spent while performing actions at one or more visited places by the one or more entities.
However, Kuhnreichi teaches creating a time log of the user location history, where the location history time log “may be organized in a table containing location data at specified time resolution (i.e. location every 1 second for example).” (para 0066). This teaches places visited by the entity over time. Kuhnreichi further teaches creating pollution data tables containing location and time information, and integrating the location information and the pollution information “to conclude the accumulated exposure over a predefined period of time,” where “the location information (i.e. coordinate and time) may be integrated with the pollution data in the database to generate exposure data; time and pollution levels meaning accumulated exposure.” (para 0067-0068). This teaches using the visited locations together with the time spent at those locations.
Kuhnreichi also teaches “accumulating pollution exposure levels in terms of level per period of time (week, month, year, etc.),” and explains that “the exposure period multiplied by the measured pollutant level equals to accumulated exposure levels.” (para 0069). This further supports total time spent at the visited places as part of the outside-activity information. Accordingly, Kuhnreichi teaches that the information associated with the outside activity includes places visited and time spent at those places. (para 0066-0069)
Therefore, before the effective filling date of the invention, it would have been obvious to one of ordinary skill in the art to incorporate Kuhnreichi’ s location-history and time-at-location information into De Luca’s indoor contamination-monitoring system in order to improve assessment of hazardous indoor locations based on not only later indoor contamination-spreading activity, but also where the user had been and for how long before entering the indoor space. De Luca teaches identifying contamination-related locations inside the production area, while Kuhnreichi teaches tracking user location history and time information to determine accumulated exposure. Combining those teachings would have predictably improved hazard identification by allowing the system to account for prior visited places and time spent there as part of the contamination-risk assessment.
Per claim 6, 13 and 20, De Luca in view of Kuhnreichi teaches wherein the identifying of the at least one location comprises: (The combination of De Luca and Kuhnreichi teaches identifying a hazardous/contaminated interior location using both indoor activity information and external exposure information. De Luca teaches identifying the location in the production area where the contamination event occurs and identifying potentially contaminated articles within a contamination zone. Kuhnreichi teaches calculating accumulated exposure from outside location/time/pollution information. (De Luca para 0014, 0016-0017; Kuhnreichi para 0066-0069.);
correlating the at least one subsequent activity with the calculated external exposure level (De Luca teaches the subsequent activity side of this limitation, namely, determining whether the individual has engaged in a contamination event while in the production area and tracking the resulting potentially contaminated articles/locations. See De Luca para 0010, 0014, 0016-0017, 0027-0030, 0038. Kuhnreichi teaches the calculated external exposure level side of this limitation, namely, integrating location information and pollution information to conclude accumulated exposure over a predefined period of time, with time and pollution levels meaning accumulated exposure. See Kuhnreichi para 0066-0069.
and identifying the at least one location with the likelihood of being hazardous based on a result of the correlating (De Luca teaches determining the location in the production area of the individual when the contamination event occurs and identifying at least one potentially contaminated article within the contamination zone produced by the contamination event. See De Luca para 0014. De Luca also teaches tracking contaminated articles/locations after the contamination event so their identity and location remain known. See De Luca para 0016. Kuhnreichi teaches using the accumulated exposure/location/pollution analysis to generate warnings that the user is exposed to high levels of pollution, is in a polluted area, or is entering a polluted area, and teaches analyzing pollution levels for specified locations such as home or office. See Kuhnreichi para 0070-0071). Accordingly, the combination teaches identifying an interior location having a likelihood of being hazardous based on the result of correlating the user’s subsequent indoor activity with the calculated external exposure level. (De Luca para 0014, 0016-0017; Kuhnreichi para 0068-0071.)
Therefore, before the effective filling date of the invention, it would have been obvious to one of ordinary skill in the art to incorporate Kuhnreichi’ s calculated external exposure level into De Luca’s indoor contamination-monitoring system so that the system could assess not only what contamination-spreading activity occurs inside the interior space, but also the degree of contamination risk associated with the user’s prior outside exposure. De Luca already teaches determining the location of an indoor contamination event and identifying contaminated areas/articles, while Kuhnreichi teaches calculating accumulated exposure from outside location/time/pollution information and using that information to analyze polluted locations and generate warnings. Combining these teachings would have predictably improved identification of interior locations likely to be hazardous or contaminated.
Per claim 8 and 15, see rejection of claim 1, and further see paragraph 0056 of De Luca that teaches memory storing one or more instructions, and paragraph 0113 and abstract that teaches processing means. Also see paragraph 0014 of Kuhnreichi that teaches memory and processing means.
Claim 7 and 14 is rejected under 35 U.S.C. 103 as being unpatentable over De Luca (US 2012/0150333) in view of Kuhnreichi (US 2013/0144527) as applied to claim 1, and further in view of Greystoke et al. (US 2015/0242930).
Per claim 7 and 14, De Luca in view of Kuhnreichi teaches determining at least one corrective action associated with the at least one location with the likelihood of being hazardous based on a weight of the at least one corrective action (De Luca teaches identifying the contaminated/hazardous interior location or contaminated article/location associated with the contamination event. (De Luca para 0014, 0016-0017.) Kuhnreichi teaches determining a corrective action for the hazardous location by comparing gathered indoor/outdoor data with a database of countermeasures. Kuhnreichi further teaches that the recommended measure may be the countermeasure that received the highest rank based on pollution sensor readings and user preset instructions. This reads on determining at least one corrective action based on a weight/rank of the corrective action. (Kuhnreichi para 0075-0076.));
providing information related to the at least one corrective action (Kuhnreichi teaches that the data provided from the system may include specific instruction regarding the functionality, location, sizes, operation modes, and timings of the countermeasure, as well as business information such as where to buy, supplier links, scientific background information, medical studies, articles, recommendations on treatments, and distribution to the user by email, banners, portal tabs, or other methods. (Kuhnreichi para 0076-0078.)).
But, De Luca in view of Kuhnreichi does not explicitly teach updating the weight of the at least one corrective action based on feedback information related to the at least one corrective action.
Greystoke teaches receiving feedback after weighted options are presented, then refining and updating the decision logic based on the feedback. Specifically: purchase options are weighed according to one or more personas (Greystoke para 0103; Fig. 5, step 506); feedback is received (Greystoke para 0105; Fig. 5, step 510); one or more personas are selectively refined based on the feedback (Greystoke para 0107; Fig. 5, step 518); the system learns from feedback and selectively updates one or more personas and search information (Greystoke para 0116-0118, 0123, 0130; Figs. 6-7). Because Greystoke’s personas and search information are what drive the weighing/ranking of presented options, Greystoke teaches updating the effective weighting/ranking basis for future recommended actions based on feedback information related to the previously presented options. Thus, the combination teaches, or at least renders obvious, updating the weight/rank of the corrective action based on feedback information related to that corrective action. (Greystoke para 0103, 0105, 0107, 0116-0118, 0123, 0130; Figs. 5-7.)).
Therefore, before the effective filling date of the invention, it would have been obvious to one of ordinary skill in the art to incorporate Greystoke’s feedback-based refinement into Kuhnreichi’ s countermeasure-recommendation system, as used with De Luca’s hazardous interior-location identification, in order to improve selection of corrective actions over time. De Luca identifies contaminated/hazardous interior locations. Kuhnreichi recommends countermeasures for those locations and ranks the recommended measures based on sensed conditions and user instructions. Greystoke teaches receiving feedback on weighted/recommended options and refining the underlying weighting logic/personas based on that feedback. Combining these teachings would have predictably improved the system by allowing corrective-action recommendations for hazardous locations to be adapted over time based on feedback concerning the usefulness, suitability, or desirability of prior recommended actions, thereby improving recommendation accuracy and tailoring future corrective-action selection.
Claim 2, 9 and 16 is rejected under 35 U.S.C. 103 as being unpatentable over De Luca (US 2012/0150333) in view of Kuhnreichi (US 2013/0144527) as applied to claim 1 and further in view of Zhang et al. (US 2020/0191913).
Per claim 2, 9 and 16, De Luca in view of Zhang teaches wherein the detecting of the one or more entities in the interior space further comprising (Zhang supplies the signal transmission / reflected signal / angular-value sensing aspects, and De Luca supplies the comparison of obtained properties with stored predefined properties for entity detection):
transmitting one or more signals towards the one or more entities (Zhang teaches that “the transmitter is configured for transmitting a first wireless signal using a plurality of transmit antennas towards an object in a venue.” (Zhang para 0028; see also para 0031 and Fig. 11 step 1102.));
receiving, in response to the one or more transmitted signals, one or more reflected signals from the one or more entities (Zhang teaches that “the receiver is configured for receiving a second wireless signal using a plurality of receive antennas,” where the second wireless signal differs from the first wireless signal due to the wireless multipath channel and “a modulation of the first wireless signal by the object.” (Zhang para 0028-0031.) This reads on receiving reflected / object-affected signals from the entity);
obtaining one or more properties of the one or more entities based on a plurality of angular values of the reflected signals (Zhang teaches that 3D information of the target can be inferred from the measurements, and that the system reconstructs the contour of the target based on estimation of the AOA and TOA of each signal reflected off the surface of the target. Zhang further teaches computing spatial spectrums for different directions and scanning the object based on those spatial directions. (Zhang para 0186-0188; Fig. 11 steps 1108-1124.) These teachings read on obtaining properties of the entity based on a plurality of angular values of the reflected signals);
comparing the one or more obtained properties of the one or more entities with a plurality of predefined properties of the one or more entities stored in a database (De Luca teaches that computer vision may use object recognition and tracking using blob analysis, texture definition, database management, and other software. (De Luca para 0053.) De Luca further teaches characterizing and matching an object to a database of objects or creating a new database describing the object. (De Luca para 0086.) De Luca also teaches identifying a person from the environment using visual features and updating/storing properties such as position, size, and color histogram. (De Luca para 0054, 0092-0093.) These teachings read on comparing obtained properties of the entity with predefined properties stored in a database); and
detecting the one or more entities based on a result of the comparing (De Luca teaches that the computer vision system may correctly identify a person from the environment using overall visual features and body-part features, and may characterize and match an object to a database of objects for tracking and identification. (De Luca para 0053-0055, 0086.) Thus, De Luca teaches detecting/identifying the entity based on the result of the comparison).
Therefore, before the effective filling date of the invention, it would have been obvious to one of ordinary skill in the art incorporate Zhang’s wireless signal-based scanning into De Luca’s contamination-monitoring system in order to improve entity detection in the monitored interior space, particularly in situations where optical-only monitoring may be limited by viewpoint, lighting, occlusion, or similar environmental conditions. De Luca already teaches identifying persons/objects in the interior space and comparing obtained characteristics to stored database information for tracking and identification. Zhang teaches transmitting wireless signals toward an object, receiving object-modulated/reflected signals, and deriving target properties from AOA/TOA-based measurements and spatial-spectrum processing. Combining these teachings would have predictably improved robustness of entity detection by providing another sensing modality for obtaining entity properties, while still using De Luca’s stored property/database comparison framework for recognition and tracking.
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure.
Hayward (US 2020/0279653) abstract and fig. 1-3
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/OMEED ALIZADA/Primary Examiner, Art Unit 2686