DETAILED ACTION
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 .
Continued Examination Under 37 CFR 1.114
A request for continued examination under 37 CFR 1.114, including the fee set forth in 37 CFR 1.17(e), was filed in this application after final rejection. Since this application is eligible for continued examination under 37 CFR 1.114, and the fee set forth in 37 CFR 1.17(e) has been timely paid, the finality of the previous Office action has been withdrawn pursuant to 37 CFR 1.114. Applicant's submission filed on 10 March 2026 has been entered and is fully considered herein.
Response to Arguments
Applicant’s arguments with respect to claims 1, 9, and 15 have been considered but are moot because the new ground of rejection does not rely on any reference applied in the prior rejection of record for any teaching or matter specifically challenged in the argument.
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 the 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, 5, 6, 9, 13-15, 19, and 20 are rejected under 35 U.S.C. § 103 as being unpatentable over US 2017/0272911 (hereinafter, “AGRAWAL”) in view of US 2014/0129560 (hereinafter, “GROKOP”), and further in view of US 2019/0285299 (hereinafter, “STEINBERG”); and US 2023/0207140 (hereinafter, “KIM”).
Regarding claim 1, claim 9, and claim 15, AGRAWAL respectively discloses:
A method, comprising: (methods 700, 800, Figs. 7, 8); A non-transitory computer-readable medium containing computer program code that, when executed by operation of one or more computer processors, performs an operation comprising: (¶ 0009: The methods disclosed may be performed by one or more of servers (including location servers), UEs, etc. using various protocols. Embodiments disclosed also relate to software, firmware, and program instructions created, stored, accessed, read or modified by processors using non-transitory computer readable media or computer readable memory; computer-readable medium 160, Figs. 1, 9); A system, comprising: (system 200, Fig. 2) one or more computer processors; and (processors 150, 950, Figs. 1, 9) a memory (memory 130, 930, Figs. 1, 9) containing a program which when executed by the one or more computer processors performs an operation, the operation comprising:
receiving environmental sensor data (sensor measurements) from a plurality of . . . ; (¶ 0122: In block 1010, Fig. 10, sensor measurements by the UE 100 may be used to determine environmental contexts associated with one or more subsets of Access Points (APs); ¶ 0098: In some embodiments, server 250 may obtain sensor measurements reported by UE 100. ¶ 0102: In some embodiments, method 800, Fig. 8, may be performed by server 250. ¶ 0103: In block 805, one or more of: visible APs, wireless measurements at the current location of UE 100, and/or sensor measurements from UE 100 at a location, may be received)
clustering a plurality of access points (APs) into a set of clusters (AP clusters) based on the environmental sensor data (sensor measurements); (¶ 0100: In block 725, Fig. 7, a subset of the visible APs that are related to the CECI may be clustered. In another embodiment, server 250 may cluster APs related to a CECI based on the received measurements and CECI determined in block 720; ¶ 0101: In block 730, the AP clusters and associated information may be reported to server 250 and/or stored in GIS database 303. (Note per ¶ 0087, GIS database 303 may be on the server; ¶ 0132: In block 1020, Fig. 10, AP clusters may be determined by associating one or more of the subsets of APs 245 above with the environmental context and an estimated location of the UE [which] corresponds to the environmental context)
generating cluster classifications (ECI - classification) by classifying each cluster in the set of clusters (AP clusters) as either an indoor cluster or an outdoor cluster, comprising:
determining . . . the environmental sensor data (sensor measurements) . . . ; and
classifying the first cluster as either an indoor cluster or an outdoor cluster based on . . . one or more defined environmental values (ECI - environmental context) (¶ 0023: ECI may include a location identifier, description, categorization, or classification of a location and/or an AP cluster (emphasis added); ¶ 0122: In block 1010, Fig. 10, sensor measurements by the UE 100 may be used to determine environmental contexts associated with one or more subsets of APs; ¶ 0090: [A]n estimated or known location of UE 100 when communicating with one or more APs in the cluster may be used as an initial approximation of the location of the AP cluster; ¶ 0080: APs visible from both outdoors and indoors (e.g. APs associated with an environmental context of both “outdoor” and “indoor”) which have higher RSSI and lower RTT2/RTT3 for the “indoor” CECI, may further be clustered with a CECI of “indoors and visible from road”. APs with higher RSSI and lower RTT for an “outdoor” CECI may be categorized as “outdoors and near building boundary”; ¶ 0068: [T]he location and one or more APs visible at the location may be associated with an “outdoor” environmental context)
assigning a respective label (ECI - label) to each respective AP in the plurality of APs based on the cluster classifications (ECI - classification); and (¶ 0022: The terms “environmental context,” or “environmental context information” or “Environmental Context Identifier (ECI),” are used to refer to an environmental characteristic. For example, the ECI may be a label, code, or description, associated with an environmental context; (¶ 0023: ECI may include a location identifier, description, categorization, or classification of a location and/or an AP cluster)
reconfiguring at least a first AP of the plurality of APs based on the assigned labels (ECI - label), (¶ 0105: In block 815, Fig. 8, ECI information associated with an AP cluster maybe updated based on the CECI. In some embodiments, APs in an AP cluster, and/or locations of one or more APs/AP clusters may be updated))
AGRAWAL does not explicitly disclose the environmental sensor data is received from the plurality of APs.
However, AGRAWAL discloses a server receiving the environmental sensor data from a plurality of UEs (¶ 0098: In some embodiments, server 250 may obtain sensor measurements reported by UE 100. ¶ 0102: In some embodiments, method 800 may be performed by server 250. ¶ 0103: In block 805, one or more of: visible APs, wireless measurements at the current location of UE 100, and/or sensor measurements from UE 100 at a location, may be received. ¶ 0107: In some embodiments, method 800 may be repeated by a plurality of UEs at various locations) and the server communicating with the plurality of UEs via the plurality of APs (¶ 0054: One or more UEs 100 may be capable of wirelessly communicating with servers 250 through one or more networks 230; ¶ 0058: As illustrated in FIG. 2, UE 100 may also communicate with server 250-1 through network 230-1 and APs 245, which may be associated with network 230-1).
It would have been obvious before the effective filing date of the claimed invention to one of ordinary skill in the art to modify the environmental sensor data of AGRAWAL to be received from the plurality of APs in the same way a communication of AGRAWAL is performed between the server and the plurality of UEs through the plurality of APs. Doing so would exploit a readily-available communication infrastructure such as network 230-1 and APs 240 with the predictable result of the environmental sensor data being communicated from the UEs to the server through the APs as disclosed by AGRAWAL, at Fig. 2, ¶¶ 0054, 0058.
AGRAWAL does not explicitly disclose:
determining a representative value of the environmental sensor data with respect to a first cluster in the set of clusters, . . .
classifying the first cluster as either an indoor cluster or an outdoor cluster based on comparing the average value to one or more defined environmental values;
In the same field of endeavor, however, GROKOP teaches:
determining a [statistical] value of the environmental sensor data with respect to a first cluster in the set of clusters, (¶ 0075: Indoor/outdoor detection: Leverage a number of low-level features to label micro-place clusters as indoor or outdoor. Features include . . . visibility of stationary Bluetooth devices based on broadcasted device ID (e.g., desktop computers, printers, etc.); target sound detection (indoor/outdoor) on an audio signal; ambient light sensor readings or recordings; camera red/green/blue (R/G/B) intensity, which gives light color from which type of light source can be determined (e.g., fluorescent vs. natural light); ¶ 0066: Compile statistics, e.g., via the statistics module 230. For each cluster discovered, associate with it relevant statistics from low-level features and inferences computed when the user was in this cluster)
classifying the first cluster as either an indoor cluster or an outdoor cluster based on comparing the [statistical] value to one or more defined environmental values; (¶¶ 0067: Assign context labels, e.g., via the context modeling module 250. For each cluster, learn a context label based on the compiled low-level feature/inference statistics. In the simplest case, this can be done by averaging the low-level features/inferences in some way. For example, computations can be performed for each cluster ID including the average fraction of time speech is detected, the average number of Bluetooth devices that are visible, and its most commonly occurring motion state. If for a particular cluster the fraction of speech is greater than some threshold, the average number of Bluetooth devices is greater than some threshold and the most common motion state is “rest,” then an appropriate context label, e.g., “in meeting,” can be assigned to the cluster. Infer context, e.g., via the context inference module 260. At a later point in time, for the user's current data stream, find the cluster with the closest matching model to the current data and output its label as the inferred context; ¶ 0075: Classify indoor vs. outdoor from current micro-place cluster)
It would have been obvious to one of ordinary skill in the art before the effective filing date of the Application, to modify AGRAWAL’s access point clustering classification procedure to provide compiling of statistical sensor data as taught by GROKOP, to improve inferred-context modeling, so that at a later point in time, for the user's current data stream, the cluster with the closest matching model to the current data can be found and its label as the inferred context can be output. See GROKOP, at ¶ 0068.
AGRAWAL does not explicitly disclose:
wherein the representative value comprises an average peak-to-peak frequency of changes in the environmental sensor data; and
In the same field of endeavor, however, STEINBERG teaches:
wherein the representative value comprises an average peak-to-peak frequency of changes in the environmental sensor data; and (¶ 0111: [D]etermine the expected rate of change or slope of inside temperature for each minute of HVAC cycle time (ΔT); ¶ 0182: Fig. 27a shows a graph of outside temperature 2702, inside temperature 2704 and HVAC cycle times 2706 in House A)
It would have been obvious to one of ordinary skill in the art before the effective filing date of the Application, to modify AGRAWAL’s access point clustering classification procedure to provide expected rate of change or slope of inside temperature as taught by STEINBERG, to provide quantification of representative environmental values, so as to enable geolocation of a device. See STEINBERG, at ¶ 0012.
AGRAWAL does not explicitly disclose:
reconfiguring . . . comprising modifying one or more wireless communication settings of the first AP.
In the same field of endeavor, however, KIM teaches:
reconfiguring comprising modifying one or more wireless communication settings of the first AP. (¶ 0090: [W]hen a smart device moves from indoors to outdoors, the smart device can automatically update the setting parameter values (for example, transmission power, number of transmissions, critical power for received signals, and the like) through the transformation of parameters; ¶ 0023: STAs 110 and 120 . . . may also be called . . . a mobile terminal, a wireless device, a wireless transmit/receive unit (WTRU), a user equipment (UE), a mobile station (MS), a mobile subscriber unit, or . . . an access point (AP); ¶ 0113: [T]echnical features of the present specification may be performed/supported through the apparatus of FIGS. 1 and/or 3)
It would have been obvious to one of ordinary skill in the art before the effective filing date of the Application, to modify AGRAWAL’s system of access point clustering classification to provide location-based transformation of parameters as taught by KIM, to improve wireless transmission in outdoor/indoor environments by accounting for “the level of the ambient interference signal [that] may be increased outdoors,” as explicitly suggested by KIM, e.g., at ¶ 0084.
Regarding claims 5, 13, and 19, the combination of AGRAWAL, GROKOP, STEINBERG, and KIM, as applied above, renders obvious claims 1, 9, and 15, respectively. AGRAWAL further discloses:
wherein clustering the plurality of APs comprises defining one or more initial cluster centroids (the centroid of the locations of the APs in the cluster) based on the one or more defined environmental values (ECI - environmental context). (¶¶ 0090, 0093: The location of the AP cluster may be approximated as the centroid of the locations of the APs in the cluster (emphasis added); ¶ 0023: ECI may identify or describe the location by a location identifier; ¶¶ 0091, 0094: APs may be clustered around an environmental context identified by an ECI)
Regarding claims 6, 14, and 20, the combination of AGRAWAL, GROKOP, STEINBERG, and KIM, as applied above, renders obvious claims 1, 9, and 15. AGRAWAL further discloses:
wherein:
a first AP of the plurality of APs indicates an indoor deployment type, and (¶ 0080: APs visible from both outdoors and indoors (e.g. APs associated with an environmental context of both “outdoor” and “indoor”) which have higher RSSI and lower RTT2/RTT3 for the “indoor” CECI, may further be clustered with a CECI of “indoors and visible from road”. APs with higher RSSI and lower RTT for an “outdoor” CECI may be categorized as “outdoors and near building boundary; ¶ 0126: An indoor environmental context associated with a fourth subset of the one or more subsets of APs 245 may be determined)
reconfiguring at least the first AP comprises configuring the first AP as an outdoor AP. (¶¶ 0080: APs visible from both outdoors and indoors (e.g. APs associated with an environmental context of both “outdoor” and “indoor”) which have higher RSSI and lower RTT2/RTT3 for the “indoor” CECI, may further be clustered with a CECI of “indoors and visible from road”. APs with higher RSSI and lower RTT for an “outdoor” CECI may be categorized as “outdoors and near building boundary”).
Claims 2, 10, and 16 are rejected under 35 U.S.C. § 103 as being unpatentable over AGRAWAL in view of GROKOP, STEINBERG, and KIM, as applied to claims 1, 9, and 15, respectively, and further in view of US 2018/0338245 (hereinafter, “TAM”).
Regarding claims 2, 10, and 16, the combination of AGRAWAL, GROKOP, STEINBERG, and KIM, as applied above, renders obvious claims 1, 9, and 15, respectively. AGRAWAL further discloses:
wherein clustering the plurality of APs into the set of clusters comprises:
generating the first cluster of APs and a second cluster of APs based on the environmental sensor data; and (¶ 0005: determining one or more AP clusters by associating the one or more subsets of APs with the at least one environmental context).
AGRAWAL does not explicitly disclose:
in response to determining that a distance between the first cluster and the second cluster is below a defined threshold, merging the clusters.
In the same field of endeavor, however, TAM teaches:
in response to determining that a distance between the first cluster and the second cluster is below a defined threshold, merging the clusters. (¶¶ 0081-0100: cluster merging process shown in Figs. 8A-8C).
It would have been obvious to one of ordinary skill in the art to modify AGRAWAL’s system of access point clustering classification by incorporating TAM’s cluster merging process, “to improve the accuracy of location estimates [and] improve the availability of location estimates by preventing the misclassification of wireless access points as moved,” as explicitly suggested by TAM, e.g., at ¶ 0083.
Claims 3, 4, 11, 12, 17, 18 are rejected under 35 U.S.C. § 103 as being unpatentable over AGRAWAL in view of GROKOP, STEINBERG, KIM, and TAM as applied above, and further in view of US 2012/0268250 (hereinafter, “KAUFMAN”) and US 2021/0360425 (hereinafter, “TRAN”).
Regarding claims 3, 11, and 17, the combination of AGRAWAL, GROKOP, STEINBERG, KIM, and TAM, as applied above, renders obvious claims 2, 10, and 16, respectively. AGRAWAL further discloses:
wherein assigning a respective label (ECI - label) to each respective AP in the plurality of APs further comprises: determining . . . (¶ 0022: The terms “environmental context,” or “environmental context information” or “Environmental Context Identifier (ECI),” are used to refer to an environmental characteristic. For example, the ECI may be a label, code, or description, associated with an environmental context; ¶ 0063: UE 100 may use stored GIS information such as SECI to determine an area category specification. The term "area category" information refers to a location description such as Parking lot, Building 20 Name, Floor numbers of APs, Relative altitude for APs, proximity to inter-floor connections in a building such as a stairway, escalator, elevator etc. (emphasis added)).
AGRAWAL does not explicitly disclose:
a minimum value, maximum value, and average value of the environmental sensor data; computing a respective score for each respective AP in the plurality of APs; and assigning a respective label to each respective AP based at least in part on the respective score.
In the same field of endeavor, however, KAUFMAN teaches:
a minimum value, maximum value, and average value of the environmental sensor data; (¶ 0034: [E]nvironmental conditions that are monitored include temperature, vibration, barometric pressure, magnetic field strength, electric field strength, exposure to light, position, humidity, ionizing radiation exposure, and/or exposure to specific chemicals. Statistical data (e.g., maximum value, minimum value, average value, mean value, differential value between multiple sensors, etc.) detailing the environmental exposure can also be determined to form additional environmental sensor data. For example, a difference between an outside and an inside temperature is determined)
It would have been obvious one to of ordinary skill in the art to modify AGRAWAL’s position-aware system by incorporating KAUFMAN’s real-time location service. Doing so provides an improved “accuracy of location estimates [and] improve the availability of location estimates by preventing the misclassification of wireless access points as moved,” as explicitly suggested by TAM, e.g., at ¶ 0083.
AGRAWAL does not explicitly disclose:
computing a respective score for each respective AP in the plurality of APs; and
assigning a respective label to each respective AP based at least in part on the respective score.
In the same field of endeavor, however, TRAN teaches:
computing a respective score (light signature) for each respective AP in the plurality of APs; and (¶ 0017: A light signature is generated based on observed frequencies of ambient light, including the frequencies of light intensity and color temperature signals; ¶ 0023: Cellular devices themselves may calculate signatures based on sensor data and report the signatures to the server components. In other cases, devices may report sensor to server components, and the server components may calculate the signatures)
assigning a respective label (i.e., indoors/outdoors) to each respective AP based at least in part on the respective score (light signature) (¶ 0058: An action 406, Fig. 4, comprises classifying a particular reporting device as being either indoors or outdoors based on the unlabeled environmental signatures 208 received from the reporting device; ¶ 0059: An action 408, Fig. 4, includes storing, with each set of operational metrics, a value or label indicating the classification (i.e., indoors or outdoors) of the device that provided the metrics).
It would have been obvious to one of ordinary skill in the art to modify AGRAWAL’s position-aware system by incorporating the calculation of environmental signatures as taught by TRAN, e.g., at ¶ 0023. In particular, one of ordinary skill in the art would have recognized TRAN’s use of a trained classification model as an obvious modification to AGRAWAL’s detection of a change in environmental contexts because both serve the purpose of providing accurate indoor/outdoor detection. Furthermore, it would have been obvious to try the modification, as it achieves the predictable result of refined indoor/outdoor positioning determinations. Therefore, it would have been obvious to one of ordinary skill in the art—before the effective filing date of the claimed invention—to use TRAN’s environmental signatures in the clustering systems of AGRAWAL and TAM according to known methods to yield the predictable result of achieving facilitating more accurate network device localization (See AGRAWAL, ¶ 0024).
Regarding claims 4, 12, and 18, the combination of AGRAWAL, GROKOP, STEINBERG, KIM, TAM, KAUFMAN, and TRAN, as applied above, renders obvious claims 3, 11, and 17, respectively. AGRAWAL does not explicitly disclose:
further comprising in response to determining that a first score of a first AP is within a defined range:
determining a deployment type indicated by the first AP; and
assigning a respective label to the first AP based on the indicated deployment type.
However, TRAN further teaches:
further comprising in response to determining that a first score (light signature) of a first AP is within a defined range: (light frequencies) (¶ 0046: An action 210, Fig. 2, comprises measuring and/or evaluating one or more light signals to create a light signature 208(b). Action 210 may comprise analyzing a light intensity signal or a light color temperature signal to detect frequencies of the light intensity signal or the light color temperature signal. More specifically, the action 210 may comprise calculating a Fourier transform of a light signal as a representation of the frequencies exhibited by the light signal, and using the Fourier transform as the light signature 208(b))
determining a deployment type (classification) indicated by the first AP; and (¶ 0058: An action 406, Fig. 4, comprises classifying a particular reporting device as being either indoors or outdoors based on the unlabeled environmental signatures 208 received from the reporting device)
assigning a respective label (indoors/outdoors) to the first AP based on the indicated deployment type. (¶ 0059: An action 408, Fig. 4, includes storing, with each set of operational metrics, a value or label indicating the classification (i.e., indoors or outdoors) of the device that provided the metrics).
It would have been obvious to one of ordinary skill in the art to modify AGRAWAL’s position-aware system by incorporating the light frequencies as taught by TRAN, e.g., at ¶ 0046. In particular, one of ordinary skill in the art would have recognized Tran’s use of a trained classification model as an obvious modification to AGRAWAL’s detection of a change in environmental contexts because both serve the purpose of providing accurate indoor/outdoor detection. Furthermore, it would have been obvious to try the modification, as it achieves the predictable result of refined indoor/outdoor positioning determinations. Therefore, it would have been obvious to one of ordinary skill in the art—before the effective filing date of the claimed invention—to use Tran’s detection of frequencies of the light intensity signal or the light color temperature signal in the clustering systems of AGRAWAL and TAM according to known methods to yield the predictable result of achieving facilitating more accurate network device localization. See AGRAWAL, at ¶ 0024.
Claims 7 and 8 are rejected under 35 U.S.C. § 103 as being unpatentable over AGRAWAL in view of GROKOP, STEINBERG, and KIM, as applied above, in view of WO 2021/252463 (hereinafter, “HOLEYANNAVAR”).
Regarding claim 7, the combination of AGRAWAL, GROKOP, STEINBERG, and KIM, as applied above, renders obvious the method of claim 1. AGRAWAL does not explicitly disclose:
wherein the environmental sensor data comprises one or more of:
(i) volatile organic compound (VOC) measurements;
(ii) carbon dioxide measurements; or
(iii) temperature measurements.
In the same field of endeavor, however, HOLEYANNAVAR teaches:
wherein the environmental sensor data comprises one or more of:
(i) volatile organic compound (VOC) measurements;
(ii) carbon dioxide measurements; . . . (¶ 0032: A micro solid state sensor adapted to detect the levels of chemical gas contaminants in parts per million (ppm) of particles of at least one of airborne molecular contaminants or volatile organic compounds, e.g., down to below two ppm and as high as 2000 ppm. In one embodiment, the micro solid state sensor is capable of detecting at least 23 different such chemical gases (AMCs and/or VOCs), e.g., carbon dioxide (CO2)).
It would have been obvious to one of ordinary skill in the art to modify AGRAWAL’s position-aware systems by incorporating a sensor capable of detecting chemical gases as taught by HOLEYANNAVAR, e.g., at ¶ 0032. In particular, one of ordinary skill in the art would have recognized that HOLEYANNAVAR’s chemical gas sensor could have been substituted for AGRAWAL’s sensor bank 180 because both serve the purpose of storing sensing environmental data. Furthermore, one of ordinary skill in the art would have been able to carry out the substitution. Finally, the substitution achieves the predictable result of locating an immediate environment of a networking device. Therefore, it would have been obvious to one of ordinary skill in the art—before the effective filing date of the claimed invention—to substitute HOLEYANNAVAR’s volatile organic compound (VOC) and carbon dioxide sensor for AGRAWAL’s sensor bank according to known methods to yield the predictable result of acquiring additional environmental data.
Regarding claim 8, the combination of AGRAWAL, GROKOP, STEINBERG, KIM, and HOLEYANNAVAR as applied above, renders obvious the method of claim 7. AGRAWAL further discloses:
wherein clustering the plurality of APs comprises, determining, for the plurality of APs, one or more of: a statistic . . . (average barometric reading) (¶ 0082: UE 100 may have information about expected or average barometric readings at a "ground level" for a location and a "floor level" CECI may be determined based on a gradient indicating a variation of the average/expected readings with height (above or below) from barometric pressure at the ground level).
AGRAWAL does not explicitly disclose:
a statistic relating to VOC measurements detected by the plurality of APs; or . . . carbon dioxide measurements detected by the plurality of APs.
In the same field of endeavor, however, HOLEYANNAVAR teaches:
a statistic relating to VOC measurements detected by the plurality of APs; or . . . carbon dioxide measurements detected by the plurality of APs. (¶ 0032: The micro solid state sensor is capable of detecting at least 23 different such chemical gases (AMCs and/or VOCs), e.g., carbon dioxide (CO2)).
It would have been obvious to one of ordinary skill in the art to modify AGRAWAL position-aware system by incorporating a sensor capable of detecting chemical gases as taught by HOLEYANNAVAR, e.g., at ¶ 0032. In particular, one of ordinary skill in the art would have recognized that HOLEYANNAVAR’s chemical gas sensor could have been substituted for AGRAWAL’s sensor bank 180 because both serve the purpose of storing sensing environmental data. Furthermore, one of ordinary skill in the art would have been able to carry out the substitution. Finally, the substitution achieves the predictable result of locating an immediate environment of a networking device. Therefore, it would have been obvious to one of ordinary skill in the art—before the effective filing date of the claimed invention—to substitute HOLEYANNAVAR’s volatile organic compound (VOC) and carbon dioxide sensor for AGRAWAL’s sensor bank according to known methods to yield the predictable result of acquiring additional environmental data.
Conclusion
The prior art made of record and not relied upon is considered pertinent to Applicant's disclosure, such as US 2019/0097904 to PARK, which discloses an aggregator identifies a maximum rate at which a data point can change between consecutive data samples. The maximum rate of change can be based on physical principles (e.g., heat transfer principles), weather patterns, or other parameters that limit the maximum rate of change of a particular data point. For example, data point 402 represents a measured outdoor air temperature and therefore can be constrained to have a rate of change less than a maximum reasonable rate of change for outdoor temperature (e.g., five degrees per minute).
Any inquiry concerning this communication or earlier communications from the Examiner should be directed to Garth D Richmond whose telephone number is (703)756-4559. The Examiner can normally be reached M-F 8 a.m. - 5 p.m. ET.
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, Kathy Wang-Hurst can be reached at 571-270-5371. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
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/GARTH D RICHMOND/Examiner, Art Unit 2644
/KATHY W WANG-HURST/Supervisory Patent Examiner, Art Unit 2644
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