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 .
DETAILED ACTION
This action is in response to the applicant's communication filed on 12/29/2023. In virtue of this communication, claims 1-20 filed on 12/29/2023 are currently pending in the instant application.
Information Disclosure Statement
The information Disclosure statement (IDS) form PTO-1449, filed on 12/29/2023 are in compliance with the provisions of CFR 1.97. Accordingly, the information disclosed therein was considered by the examiner.
Drawings
The drawings were received on 12/29/2023 have been reviewed by Examiner and they are acceptable.
Double Patenting
The nonstatutory double patenting rejection is based on a judicially created doctrine grounded in public policy (a policy reflected in the statute) so as to prevent the unjustified or improper timewise extension of the “right to exclude” granted by a patent and to prevent possible harassment by multiple assignees. A nonstatutory double patenting rejection is appropriate where the conflicting claims are not identical, but at least one examined application claim is not patentably distinct from the reference claim(s) because the examined application claim is either anticipated by, or would have been obvious over, the reference claim(s). See, e.g., In re Berg, 140 F.3d 1428, 46 USPQ2d 1226 (Fed. Cir. 1998); In re Goodman, 11 F.3d 1046, 29 USPQ2d 2010 (Fed. Cir. 1993); In re Longi, 759 F.2d 887, 225 USPQ 645 (Fed. Cir. 1985); In re Van Ornum, 686 F.2d 937, 214 USPQ 761 (CCPA 1982); In re Vogel, 422 F.2d 438, 164 USPQ 619 (CCPA 1970); In re Thorington, 418 F.2d 528, 163 USPQ 644 (CCPA 1969).
A timely filed terminal disclaimer in compliance with 37 CFR 1.321(c) or 1.321(d) may be used to overcome an actual or provisional rejection based on nonstatutory double patenting provided the reference application or patent either is shown to be commonly owned with the examined application, or claims an invention made as a result of activities undertaken within the scope of a joint research agreement. See MPEP § 717.02 for applications subject to examination under the first inventor to file provisions of the AIA as explained in MPEP § 2159. See MPEP § 2146 et seq. for applications not subject to examination under the first inventor to file provisions of the AIA . A terminal disclaimer must be signed in compliance with 37 CFR 1.321(b).
The filing of a terminal disclaimer by itself is not a complete reply to a nonstatutory double patenting (NSDP) rejection. A complete reply requires that the terminal disclaimer be accompanied by a reply requesting reconsideration of the prior Office action. Even where the NSDP rejection is provisional the reply must be complete. See MPEP § 804, subsection I.B.1. For a reply to a non-final Office action, see 37 CFR 1.111(a). For a reply to final Office action, see 37 CFR 1.113(c). A request for reconsideration while not provided for in 37 CFR 1.113(c) may be filed after final for consideration. See MPEP §§ 706.07(e) and 714.13.
The USPTO Internet website contains terminal disclaimer forms which may be used. Please visit www.uspto.gov/patent/patents-forms. The actual filing date of the application in which the form is filed determines what form (e.g., PTO/SB/25, PTO/SB/26, PTO/AIA /25, or PTO/AIA /26) should be used. A web-based eTerminal Disclaimer may be filled out completely online using web-screens. An eTerminal Disclaimer that meets all requirements is auto-processed and approved immediately upon submission. For more information about eTerminal Disclaimers, refer to www.uspto.gov/patents/apply/applying-online/eterminal-disclaimer.
Claim 1-20 of instant application, are rejected on the ground of nonstatutory double patenting as being unpatentable over claim 1-12 of U.S. Patent No. 12,118796, in view of Leonard et al. (US 2016/0217384). Although the claims are not identical, they are not patentably distinct from each other as shown below.
Consider independent claim 1 of the instant application, claim 1 of the co-owned applications disclose all the claim limitation, However, claims 1 of the co-owned applications do not recite the following limitation “determine an amount of expected exit counts for the first period of time based on the historical ingress and egress data of the second period of time by fitting the historical ingress and egress data to a first probability distribution.” of claim 1 of the instant application. However, as set forth below in the rejection of claim 1 of the instant application under 35 U.S.C 103 (a). The combination of Ding as modified by Leonard disclose the limitation of claim 1 of the instant application, which are missing from claim 1 of co-owned applications. In addition, a motivation statement to combine the Ding and Leonard references are provided in the next section, as well. Therefore, it would have been obvious to combine Claim of Co-owned applications listed above with Ding and Leonard to arrive at claim 1 of the instant application.
The conflicting claims are not identical because the embodiments of co-owned claims omit steps not explicitly required by the embodiment of instant claims. However, the conflicting claims are not patentably distinct from each other because:
· Instant claims and co-owned claims recite common subject matter;
· Instant claims, which recite the open ended transitional phrase “comprising,” does not preclude the difference in steps recited by co-owned claims, and
. the elements of instant claims are obvious over co-owned claims, and completely anticipate the subject matter of instant claim, and “anticipation is the epitome of obviousness” Connell v. Sears, Roebuck & Co.,722 F.2d 1542, 1548, 220 USPQ 193, 198 (Fed. Cir. 1983) (citing In re Fracalossi, 681F.2d 792, 215 USPQ 569 (CCPA 1982)).
Please refer to comparison table below for a better comparison of claims between claims 1-20 of the instant application and claims 1-12 of the U.S Patent No. 12,118796.
Instant Application 18/575542
Co-Owned Application 12,118796
1. A vision system for detecting objects in an environment, comprising: at least one sensor; a memory; and a processor communicatively coupled with the memory and the at least one sensor and configured to: detect, using the at least one sensor, persons that entered and exited the environment during a first period of time; determine an entry count and an exit count for the first period of time; retrieve, from a database, historical ingress and egress data comprising entry and exit counts of the environment for a second period of time; determine an amount of expected exit counts for the first period of time based on the historical ingress and egress data of the second period of time by fitting the historical ingress and egress data to a first probability distribution; update the exit count for the first period of time to the amount of expected exit counts; and store the updated exit count in the database.
6. The vision system of claim 1, wherein the first period of time and the second period of time are at a same time of day across different days.
7. The vision system of claim 6, wherein the different days are a same day across different weeks.
8. The vision system of claim 1, wherein the processor is further configured to: transmit an alert to the administrator; or automatically prevent further entries.
1. A vision system for detecting persons exiting an environment, comprising: at least one sensor; a memory; and a processor communicatively coupled with the memory and the at least one sensor and configured to: detect, using the at least one sensor, persons that exited the environment during a first period of time; determine an exit count for the first period of time; retrieve, from a database in the memory, historical egress data for a second period of time corresponding to the first period of time, wherein the historical egress data comprises a detected historic exit count and a corrected historic exit count of the environment; calculate an error rate for the second period of time based on a ratio of the detected historic exit count and the corrected historic exit count; determine a corrected exit count for the first period of time by adjusting the exit count using the error rate; and store the corrected exit count in the database.
3. The vision system of claim 1, wherein the first period of time and the second period of time are at a same time of day across different days.
4. The vision system of claim 3, wherein the different days are a same day across different weeks.
5. The vision system of claim 1, wherein the processor is further configured to: transmit an alert to a user indicating the corrected exit count.
Similarly, claims 1-20 of instant application, are provisionally rejected on the ground of nonstatutory double patenting as being unpatentable over claim 1-20 of the continuation of the above US patent, co-pending application 18/886,611, in view of Leonard et al. (US 2016/0217384). The claims have been rejected for the same reasons indicated in the parent application above.
Consider independent claim 1 of the instant application, claim 1 of the co-owned applications disclose all the claim limitation, However, claims 1 of the co-owned applications do not recite the following limitation “determine an amount of expected exit counts for the first period of time based on the historical ingress and egress data of the second period of time by fitting the historical ingress and egress data to a first probability distribution.” of claim 1 of the instant application. However, as set forth below in the rejection of claim 1 of the instant application under 35 U.S.C 103 (a). The combination of Ding as modified by Leonard disclose the limitation of claim 1 of the instant application, which are missing from claim 1 of co-owned applications. In addition, a motivation statement to combine the Ding and Leonard references are provided in the next section, as well. Therefore, it would have been obvious to combine Claim of Co-owned applications listed above with Ding and Leonard to arrive at claim 1 of the instant application.
Please refer to comparison table below for a better comparison of claims between claims 1-20 of the instant application and claims 1-20 of the U.S Patent No. 18/886,611.
Instant Application 18/575542
Co-Owned Application 18/886611
1. A vision system for detecting objects in an environment, comprising: at least one sensor; a memory; and a processor communicatively coupled with the memory and the at least one sensor and configured to: detect, using the at least one sensor, persons that entered and exited the environment during a first period of time; determine an entry count and an exit count for the first period of time; retrieve, from a database, historical ingress and egress data comprising entry and exit counts of the environment for a second period of time; determine an amount of expected exit counts for the first period of time based on the historical ingress and egress data of the second period of time by fitting the historical ingress and egress data to a first probability distribution; update the exit count for the first period of time to the amount of expected exit counts; and store the updated exit count in the database.
6. The vision system of claim 1, wherein the first period of time and the second period of time are at a same time of day across different days.
7. The vision system of claim 6, wherein the different days are a same day across different weeks.
8. The vision system of claim 1, wherein the processor is further configured to: transmit an alert to the administrator; or automatically prevent further entries.
A vision system for detecting objects moving in an environment, comprising: at least one sensor; at least one memory; and at least one hardware processor communicatively coupled with the at least one memory and the at least one sensor and configured, individually or in combination, to: detect, using the at least one sensor, objects that crossed a boundary of an environment during a first period of time; determine, for the first period of time, a count of objects that crossed the boundary in a first direction; retrieve, from a database in the memory, historical egress and ingress data for a second period of time corresponding to the first period of time, wherein the historical egress and ingress data comprises a detected historic count of crossings in the first direction and a corrected historic count of crossings in the first direction; calculate an error rate for the second period of time based on a ratio of the detected historic count and the corrected historic count; determine a corrected count for the first period of time by adjusting the count using the error rate; and store the corrected count in the database.
5. The vision system of claim 1, wherein the first period of time and the second period of time are at a same time of day across different days.
6. The vision system of claim 3, wherein the different days are a same day across different weeks.
7. The vision system of claim 1, wherein the processor is further configured to: transmit an alert to a user indicating the corrected exit count.
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.
Claims 17-20 are rejected under 35 U.S.C. § 101 because the claims are directed to non-statutory subject matter in the form of a “computer readable medium.” The claims fall outside the scope of patent-eligible subject matter at least because the claimed computer readable medium is broad enough to encompass non-transitory embodiments. (E.g., one of ordinary skill in the art could reasonably be expected to interpret the claimed computer readable medium as a carrier wave onto which instructions could be coded.)
See also the Mentor Graphics v. EVE-USA, Inc., 851 F.3d 1275, 112 USPQ2d 1120 (Fed. Cir. 2017) “Subject Matter Eligibility of Computer Readable Media” which states in relevant part “[i]n an effort to assist the patent community in overcoming a rejection or potential rejection under 35 U.S.C. § 101 in this situation, the USPTO suggests the following approach. A claim drawn to such a computer readable medium that covers both transitory and non-transitory embodiments may be amended to narrow the claim to cover only statutory embodiments to avoid a rejection under 35 U.S.C. § 101 by adding the limitation ‘non-transitory’ to the claim.”
Therefore, an amendment applicable to the claims, consistent with the recommendations in the above-noted Mentor Graphics v. EVE-USA, Inc., that would overcome the instant ‘101 rejection, follows:
Claim 17 (Amended) A non-transitory computer readable medium …
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) 1-4, 8-12, and 16-20 is/are rejected under 35 U.S.C. 103 as being unpatentable over Ding et al. (US 2020/0380252), in view of Leonard et al. (US 2016/0217384).
As per claim 1, A vision system for detecting objects in an environment, comprising: at least one sensor; a memory; and a processor communicatively coupled with the memory and the at least one sensor and configured to:
“detect, using the at least one sensor, persons that entered and exited the environment during a first period of time;” (Ding, ¶[0027], ¶[0032] discloses one or more video cameras may capture video footage of an entrance area, an exit area, and/or an area within and/or inside the retail facility for over a period of time. As such, a video camera may be capture video footages of people walking into and/or out of the retail facility at the entrance area and/or at the exit area. In some embodiments, in cooperation with a control circuit operating in a neural network configuration and/or architecture, the captured video footages may be used to detect egress at an entrance area of a retail facility, count humans/people at an area inside the retail facility, and determine a count of humans/people inside the retail facility at a particular period of time. [0053-0054] )
“determine an entry count and an exit count for the first period of time;” (Ding, Figures 5 and 6, ¶[0055-0056] discloses the control circuit 102 may define an inbound value 506 as a total number of detected and tracked humans entering the retail facility 118 at the entrance area 502 and the exit area 504. By one approach, the control circuit 102 may define an outbound value 508 as a total number of detected and tracked humans exiting the retail facility 118 at the entrance area 502 and the exit area 504. In one configuration, the control circuit 102 may define a count 520 as a total number of people inside the retail facility 118. The control circuit 102 may subtract the outbound value 508 from the inbound value 506 to determine the count 520 of people inside the retail facility 118, at step 918. ¶[0057] discloses the control circuit 102 may store the count 520 over a period of time to a database coupled to the control circuit 102. )
“Storing the counts for the period of time in a database”(Ding, ¶[0026] disclose the neural network model may be trained using stored footage from one of the first video camera, the second video camera, and a similar video camera having similar resolution and point of view. ¶[0027] discloses include storing, by the control circuit, the count over a period of time to a database coupled to the control circuit. By one approach, the method may include causing, by the control circuit, an electronic device to display the count.¶[0057] discloses the control circuit 102 may store the count 520 over a period of time to a database coupled to the control circuit 102.)
However Ding does not explicitly disclose the following which would have been obvious in view of Leonard from similar filed of endeavor “retrieve, from a database, historical ingress and egress data comprising entry and exit counts of the environment for a second period of time;”(Leonard, ¶[0167] discloses a time series data store 2330, a probability distributions data store 2340, a selection criterion data store 2350, and a forecast data store 2360, data can be maintained, derived, or otherwise accessed from various data stores. ¶[0170] discloses time series analysis engine may utilize the user specification to identify a data set from time series data store 2330, a data store responsible for storing time series data sets. The time series analysis engine 2316 may analyze the time series data set to provide a set of counts. The time series analysis engine 2316 may provide the set of counts and the user input to the probability distribution selector engine 2318. Alternative, the probability distribution selector engine 2318 may analyze the time series data set to provide the set of counts.)
“determine an amount of expected exit counts for the first period of time based on the historical ingress and egress data of the second period of time by fitting the historical ingress and egress data to a first probability distribution;” (Leonard, ¶[0007] discloses receive a time series data set, wherein the time series data set includes a plurality of data points that correspond to a plurality of discrete values. The instructions further cause the data processing apparatus to generate a set of counts for the time series data set by analyzing the time series data. generate the set of predicted future data points(expected future count) for the time series data set, wherein generating the set of predicted future data points includes using the selected statistical model. adjust the set of predicted future data points for the time series data set, wherein adjusting the set of predicted future data points includes using the set of parameters corresponding to the optimal discrete probability distribution. ¶[0172] discloses the forecast generator 2320 may utilize the user input to determine a number of statistical models with which to provide a forecast. The statistical models may be stored in forecast data store. ¶[0173] discloses the time series analysis engine 2316 may use information related to the probability distribution elected by the probability distribution selector engine 2318 to adjust the forecast selected by the forecast generator 2320 ¶[0177] discloses may fit the set of candidate probability distributions to the set of counts for the time series data set. The fit may then be evaluated (e.g., by the probability distribution selector engine 2318) based on the specified selection criterion. A probability distribution having a best fit, for example, may be selected by the probability distribution selector engine 2318. )
“update the exit count for the first period of time to the amount of expected exit counts; and store the updated exit count in the database.”( Leonard, ¶[0007] discloses adjust the set of predicted future data points for the time series data set. ¶[0173] discloses The time series analysis engine 2316 may use information related to the probability distribution elected by the probability distribution selector engine 2318 to adjust the forecast selected by the forecast generator 2320. ¶[0195] discloses adjustments may be made to the generated time series at 2752 to produce a count series forecast 2754. ¶[0196] discloses the count series forecast may be stored for later use.)
Before the effective filing date of the claimed invention it would have been obvious to a person of ordinary skill in the art to combine Leonard technique of utilizing time series count dataset into Ding technique to provide the known and expected uses and benefits of Leonard technique over determining a count of people at a retail facility technique of Ding. The proposed combination would have constituted a mere arrangement of old elements with each performing their known function, the combination yielding no more than one would expect from such an arrangement.
Therefore, it would have been obvious to a person of ordinary skill in the art to incorporate Leonard to Ding in order to accurately predict future events with accurate count dataset. (Refer to Leonard paragraph [0004].)
Claims 9 and 17 have been analyzed and are rejected for the reasons indicated in claim 1 above. Additionally, the rationale and motivation to combine the Ding and Leonard references, presented in rejection of claim 1, apply to these claims.
As per claim 2, The vision system of claim 1, “wherein the processor is further configured to: determine an amount of entry counts for the first period of time”( Ding, ¶[0055] discloses the control circuit 102 may define an inbound value 506 as a total number of detected and tracked humans entering the retail facility 118 at the entrance area 502 and the exit area 504.)
“wherein the updated entry count is used to determine the amount of exit counts” ¶[0055-0056] discloses the control circuit 102 may define an inbound value 506 as a total number of detected and tracked humans entering the retail facility 118 at the entrance area 502 and the exit area 504. By one approach, the control circuit 102 may define an outbound value 508 as a total number of detected and tracked humans exiting the retail facility 118 at the entrance area 502 and the exit area 504. In one configuration, the control circuit 102 may define a count 520 as a total number of people inside the retail facility 118. The control circuit 102 may subtract the outbound value 508 from the inbound value 506 to determine the count 520 of people inside the retail facility 118, at step 918.)
“store the updated entry count in the database” (Ding, ¶[0057] discloses the control circuit 102 may store the count 520 over a period of time to a database coupled to the control circuit 102.)
“determine an amount of expected counts by fitting historical ingress data of the second period of time to a second probability distribution;”(Leonard, ¶[0170] discloses identify a data set from time series data store 2330, a data store responsible for storing time series data sets. The time series analysis engine 2316 may analyze the time series data set to provide a set of counts. ¶[0171] discloses The probability distribution selector engine 2318 may further identify a number of rules, formulas, and/or algorithms corresponding to a selection criterion (e.g., AIC, BIC, log-likelihood, or the like) from the selection criterion data store 2350. The probability distribution selector engine 2318 may utilize the information corresponding to the number of probability distributions and the selection criterion to determine an optimal probability distribution for the time series data set. Information related to the optimal probability distribution may be provided to the time series analysis engine 2316.¶[0172-0173] discloses the time series analysis engine 2316 may cause the forecast generator 2320 to provide a forecast for the time series data set. In at least one example, the time series analysis engine 2316 may provide the time series data set and the user input to the forecast generator 2320. The forecast generator 2320 may utilize the user input to determine a number of statistical models with which to provide a forecast. In at least one example, the statistical models may be stored in forecast data store 2360, a data store responsible for storing such information. )
“wherein the updated entry count is used to determine the amount of expected exit counts;” (Leonard, ¶[0043] Predictive modeling can refer to a number of techniques used in predictive analytics that have a common goal of finding a relationship between a target, a response (e.g., a dependent variable), and various predictors (e.g., an independent variables. ¶[0155] discloses a data specification may include: a number of dependent variables (e.g., target variable or variable to forecast); a number of independent variables (e.g., input variable or predictor variable); a number of adjustment variables (e.g., systematic variables). then in ¶[0188] discloses the time series data preparation specification 2704 may be user-specified and may include, for example, a target variable, a response (e.g., a dependent variable, and one or more predictor variables (e.g., independent variables) .
“update the entry count for the first period of time to the amount of expected entry counts and store the updated entry count in the database.” (Leonard, ¶[0173] discloses the time series analysis engine 2316 may receive or obtain the forecast generated and selected by the forecast generator 2320. The time series analysis engine 2316 may use information related to the probability distribution elected by the probability distribution selector engine 2318 to adjust the forecast selected by the forecast generator 2320. ¶[0195] discloses adjustments may be made to the generated time series at 2752 to produce a count series forecast 2754. ¶[0196] discloses the count series forecast may be stored for later use.)
Claims 10 and 18 have been analyzed and are rejected for the reasons indicated in claim 2 above.
As per claim 3, The vision system of claim 2, “wherein the second probability distribution is a Poisson distribution.”(Leonard, ¶[0178] discloses the zero-modified Poisson distribution selected for the optimal probability distribution.)
Claims 11 and 19 have been analyzed and are rejected for the reasons indicated in claim 3 above.
As per claim 4, The vision system of claim 3, “wherein the processor is configured to determine the amount of expected entry counts by: determining that the entry count is zero; calculating a probability of the entry count being zero using logistical regression on the historical ingress data; and determining the amount of expected entry counts using a Zero-Inflated Poisson (ZIP) distribution.”(Leonard, ¶[0047] discloses using discrete probability distributions with count series analysis can better predict future values, and, most importantly, more realistic confidence intervals than current techniques. In addition, some discrete probability distributions have zero-modified versions where there are more or fewer zero values than expected under the usual unmodified version of the distribution. As discussed herein, “zero-modified” is intended to refer to a zero-inflated probability distribution (e.g., a probability distribution having an excess of zero values) and/or a zero-deflated probability distribution (e.g., a probability distribution where zero values are discarded or otherwise ignored, or a probability distribution where zero values are less numerous than expected). Further see ¶[0180].)
Claims 12 and 20 have been analyzed and are rejected for the reasons indicated in claim 4 above.
As per claim 8, The vision system of claim 1, “wherein the processor is further configured to: transmit an alert to the administrator; or automatically prevent further entries.”(Ding, ¶[0021] discloses an alert message that indicates a value of the count. For example, the alert message may include a store identifier, a camera identifier, and/or an area identifier. Further see ¶[0051].)
Claim 16 has been analyzed and is rejected for the reasons indicated in claim 8 above.
Claim(s) 5 and 13 is/are rejected under 35 U.S.C. 103 as being unpatentable over Ding et al. (US 2020/0380252), in view of Leonard et al. (US 2016/0217384), further in view of Willemain et al. (US 2015/0161545).
As per claim 5, The vision system of claim 1, However Ding as modified by Leonard does not explicitly disclose the following which would have been obvious in view of Willemain from similar filed of endeavor “wherein the first probability distribution is a Beta-Binomial distribution.”( Willemain, ¶[0042] discloses a beta-binomial probability model is used to estimate the distribution of coincidences. ¶[0043] discloses the coincidence probability for the two items is determined by summing the probability values from the beta-binomial distribution for C or more coincidences.)
Before the effective filing date of the claimed invention it would have been obvious to a person of ordinary skill in the art to combine Willemain technique of estimating probability of an event into Ding as modified Leonard technique to provide the known and expected uses and benefits of Willemain technique over determining a count of people at a retail facility technique of Ding as modified Leonard. The proposed combination would have constituted a mere arrangement of old elements with each performing their known function, the combination yielding no more than one would expect from such an arrangement.
Therefore, it would have been obvious to a person of ordinary skill in the art to incorporate Willemain to Ding as modified Leonard in order to accurately analyze and predict data. (Refer to Willemain paragraph [0003].)
Claim 13 has been analyzed and is rejected for the reasons indicated in claim 5 above.
Claim(s) 6-7, 14-15 is/are rejected under 35 U.S.C. 103 as being unpatentable over Ding et al. (US 2020/0380252), in view of Leonard et al. (US 2016/0217384), in view of Anand et al. (US 2019/0260688).
As per claim 6, The vision system of claim 1, However Ding as modified by Leonard does not explicitly disclose the following which would have been obvious in view of Annand from similar filed of endeavor “wherein the first period of time and the second period of time are at a same time of day across different days.” (Anand, ¶[0016] discloses he performance analysis platform may normalize the multidimensional time series data across a set of points in time (e.g., for a same day each week, for a same time each day, etc.). ¶[0048] discloses the multidimensional time series data may be time series data received over a period of time, received for the same time period across different historical points in time (e.g., for the same time of day across different days.)
Before the effective filing date of the claimed invention it would have been obvious to a person of ordinary skill in the art to combine Anand technique of analysis of time series data into Ding as modified Leonard technique to provide the known and expected uses and benefits of Anand technique over determining a count of people at a retail facility technique of Ding as modified Leonard. The proposed combination would have constituted a mere arrangement of old elements with each performing their known function, the combination yielding no more than one would expect from such an arrangement.
Therefore, it would have been obvious to a person of ordinary skill in the art to incorporate Anand to Ding as modified Leonard in order to accurately indicating occurrence of an event or detecting expected pattern in the event. (Refer to Anand paragraph [0002].)
Claim 14 has been analyzed and is rejected for the reasons indicated in claim 6 above.
As per claim 7, The vision system of claim 6, Ding as modified by Leonard as modified by Anand further disclose “wherein the different days are a same day across different weeks.”( Anand, ¶[0015] discloses the multidimensional time series data may include data received over a period of time, data received for the same time period across different historical points in time (e.g., the same time across different days, the same day in different weeks, etc.). ¶[0048] discloses the multidimensional time series data may be time series data received over a period of time, received for the same time period across different historical points in time (e.g., for the same time of day across different days, for the same day of the week across different weeks, etc.), and/or the like. )
Claim 15 has been analyzed and is rejected for the reasons indicated in claim 7 above.
Contact
Any inquiry concerning this communication or earlier communications from the examiner should be directed to SHAGHAYEGH AZIMA whose telephone number is (571)272-1459. The examiner can normally be reached Monday-Friday, 9:30-6:30.
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/SHAGHAYEGH AZIMA/Examiner, Art Unit 2671