DETAILED ACTION
The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA .
Claims 1-20 are pending for examination.
Information Disclosure Statement
The information disclosure statement (IDS) submitted on 01/28/2026 and 02/28/2024 are in compliance with the provisions of 37 CFR 1.97. Accordingly, the information disclosure statement is being considered by the examiner.
Claim Rejections - 35 USC § 112
The following is a quotation of 35 U.S.C. 112(b):
(b) CONCLUSION.—The specification shall conclude with one or more claims particularly pointing out and distinctly claiming the subject matter which the inventor or a joint inventor regards as the invention.
Claims 4 and 6-7 are rejected under 35 U.S.C. 112(b) or 35 U.S.C. 112 (pre-AIA ), second paragraph, as being indefinite for failing to particularly point out and distinctly claim the subject matter which the inventor or a joint inventor, or for pre-AIA the applicant regards as the invention.
In claim 4, it is not clear and ambiguous to recite “which are targets of selection”. How are targets of selection related to models? Also, it is not clear and ambiguous to recite “trained by dividing ….. assumable maximum estimated number of targets”. How are these functions interrelated for performing the claimed functions.
In claim 6 (lines 9-12), it is unclear and ambiguous to recite “using certainty … as input information”. How is this certainty erformed?
In claim 7 (line 5), it is unclear and ambiguous what “labeled in the same manner between different models” means.
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 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 set forth in Graham v. John Deere Co., 383 U.S. 1, 148 USPQ 459 (1966), that are applied 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 non-obviousness.
This application currently names joint inventors. In considering patentability of the claims the examiner presumes that the subject matter of the various claims was commonly owned as of the effective filing date of the claimed invention(s) absent any evidence to the contrary. Applicant is advised of the obligation under 37 CFR 1.56 to point out the inventor and effective filing dates of each claim that was not commonly owned as of the effective filing date of the later invention in order for the examiner to consider the applicability of 35 U.S.C. 102(b)(2)(C) for any potential 35 U.S.C. 102(a)(2) prior art against the later invention.
Claims 1-3, 8-10, and 15-17 are rejected under 35 U.S.C. 103(a) as being unpatentable over Takeuchi (US 2021/0227486 A1), hereinafter referred to as Takeuchi, in view of Fletcher (US 10,374,646 B2), hereinafter referred to as Fletcher, and further in view of Chandran et al. (US 2021/0012506 A1) hereinafter referred to as Chandran.
Regarding claim 1:
Takeuchi discloses a number-of-target estimation system (quantity-of-people estimation system [0070]) comprising:
a first number-of-target estimation unit configured to estimate the number of targets existing in a first area based on radio information ({first} terminal path estimation unit receives radio wave detection information and estimates traveling paths of wireless terminals in target area [0072] or estimating quantity of people, e.g., target, on all paths [0095]) or image information excluding wireless propagation path information, which is related to the first area (or image data from camera related to target area [0123]);
a second number-of-target estimation unit configured to perform a final number-of-target estimation (actual quantity-of-people estimation unit, e.g., second number-of-target estimation unit, estimates the actual quantity of people in whole target area [0075]), using a result of the estimation of the first number-of-target estimation unit as input information (using information related to quantity of terminals per traveling path in whole target area – which is estimated by terminal path estimation unit, e.g., first number-of-target estimation unit [0075] and terminal detection rate per fixed point photographed by cameras – which is calculated by terminal detection rate calculation unit, e.g., acquisition unit [0075]);
Also, Takeuchi discloses the terminal detection rate calculation unit, e.g., acquisition unit, calculates the quantity of people, whose terminals are undetected or the quantity of people whose traveling paths are uncertain [0117]. However, Takeuchi does not specify the acquisition unit is a wireless propagation path acquisition unit configured to acquire wireless propagation path information, based on a radio signal transmitted from a radio in the first area.
Fletcher, from the same field of endeavor, teaches the acquisition unit is a wireless propagation path acquisition unit configured to acquire wireless propagation path information, based on a radio signal transmitted from a radio in the first area (digital signal processor calculates loss coefficient, e.g., propagation path information, associated with wireless signals transmitted by transmitting devices [col.7, lines based on power levels [col.7, lines 30-34]).
Therefore, it would have been obvious to acquire the propagation path information for determining the number of targets; thus obtaining the basis for targets counting - Fletcher [col.5, lines 53-59].
Takeuchi in view of Fletcher does not further indicate that a second number-of-target estimation unit configured to perform a final number-of-target estimation by machine learning using a result of the wireless propagation path information acquired by the wireless propagation path acquisition unit as input information, wherein the second number-of-target estimation unit is configured to select models to be used for the estimation of the machine learning, based on the result of the estimation of the first number-of-target estimation unit.
Chandran, from the same field of endeavor, teaches a second number-of-target estimation unit configured to perform a final number-of-target estimation by machine learning (crowd estimation technique integration module, e.g., second number-of-target estimation unit, estimates a crowd count in accordance with selected plurality of crowd estimation techniques, e.g., via machine learning [0006]) using a result of acquisition as input information (using acquisition of confidence values as input information, elements 1012 in Fig.1000] wherein the second number-of-target estimation unit is configured to select models to be used for the estimation of the machine learning, based on the result of the estimation of the first number-of-target estimation unit ( wherein crowd estimation technique integration module selects selecting crowd estimation techniques, e.g., models, in response to performance modeling of estimation techniques [0006]).
Therefore, it would have been obvious to one of ordinary skill in the art at the time before the claimed invention was filed to select models to be used for the estimation of the number of targets using the machine learning mode, based on results derived from other estimation units, e.g., number of targets based on radio information or image, and propagation path information; thus improving accuracy of targets estimation in a variety of crowd conditions and crowd locations - Chandran [0004].
Regarding claim 2:
Takeuchi in view of Fletcher and Chandran discloses all limitations of claim 1.
Takeuchi further discloses the first number-of-target estimation unit is configured to estimate the number of targets, based on the number of terminal connections to an access point (estimating quantity of people on all paths [0095], e.g., connections, to base station [0072]), which is managed at the access point forming a service area of a wireless network in the first area (base station forms traveling paths with wireless terminals in target area [0072]).
Regarding claim 3:
Takeuchi in view of Fletcher and Chandran discloses all limitations of claim 1.
Takeuchi further discloses the first number-of-target estimation unit is configured to analyze an image obtained by capturing the first area and estimates the number of targets (analyze image by using images acquired in cameras and estimate quantity of people in target area [0073 and 0086]).
Regarding claim 8:
Claim 8 is rejected for substantially same reason as applied to claim 1 above, except that claim 8 is in a method claim format.
Regarding claims 9-10:
Claims 9-10 are rejected for substantially same reason as applied to claims 2-3 above, respectively, except that claims 9-10 are in a method claim format.
Regarding claim 15:
Claim 15 is rejected for substantially same reason as applied to claim 1 above, except that claim 15 is in a non-transitory computer-readable storage medium format.
Regarding claims 16-17:
Claims 16-17are rejected for substantially same reason as applied to claims 2-3 above, respectively, except that claims 16-17 are in a non-transitory computer-readable storage medium format.
Claims 4, 7, 11, 14, and 18 are rejected under 35 U.S.C. 103(a) as being unpatentable over Takeuchi in view of Fletcher and Chandran, as applied to claims 1, 8, and 15 above, respectively, and further in view of Chalkidis et al. (US 2024/0320507 A1) hereinafter referred to as Chalkidis.
Regarding claim 4:
Takeuchi in view of Fletcher and Chandran discloses all limitations of claim 1.
Takeuchi in view of Fletcher does not further disclose, while Chandran further teaches the models to be used for the estimation of the machine learning which are targets of selection of the second number-of-target estimation unit, (modeling crowd estimation techniques # 1, #2, #3 to estimate crowd count [0005]), are a plurality of independent models trained (crowd estimation techniques # 1, #2, #3).
Takeuchi in view of Fletcher and Chandran does not further teach
models are trained by dividing wireless propagation path information labeled with each number of targets within a range up to an assumable maximum estimated number of targets, and inputting different combinations of data set groups among the plurality of data sets.
Chalkidis, from the same field of endeavor, teaches models are trained by dividing wireless propagation path information labeled with each number of targets within a range up to an assumable maximum estimated number of targets (dividing dataset data into a plurality of partitions according to a set of thresholds determined based on group of labels [0168]), and inputting different combinations of data set groups among the plurality of data sets (inputting subset of training dataset [ 0072]).
Therefore, it would have been obvious to one of ordinary skill in the art at the time before the claimed invention was filed to train a plurality of independent models by dividing the propagation path information labeled with each number of targets within a range up to an assumable maximum estimated number of targets; thus ensuring each model has to master one specific mode of data and not being overwhelmed by too many targets.
Regarding claim 7:
Takeuchi in view of Fletcher and Chandran and Chalkidis discloses all limitations of claim 4.
Takeuchi in view of Fletcher and Chandran does not, while Chalkidis further teaches the plurality of independent models are capable of inputting a data set of wireless propagation path information labeled in the same manner between different models (inputting data in dataset with labels of same count of number to different models, elements 910, 935 in Fig.9 and [0096]).
Therefore, it would have been obvious to one of ordinary skill in the art at the time before the claimed invention was filed to input a data set of wireless propagation path information labeled in the same manner between different models; thus being able to determine the loss functions across all models being identical.
Regarding claims 11 and 14:
Claims 11 and 14 are rejected for substantially same reason as applied to claims 4 and 7 above, respectively, except that claims 11 and 14 are in a method claim format.
Regarding claim 18:
Claim 18 is rejected for substantially same reason as applied to claim 4 above, except that claim 18 is in a non-transitory computer-readable storage medium format.
Claims 5, 12, and 19 are rejected under 35 U.S.C. 103(a) as being unpatentable over Takeuchi in view of Fletcher and Chandran and Chalkidis, as applied to claims 4, 11, and 18 above, and further in view of Bender et al. (US 2020/0007915 A1) hereinafter referred to as Bender.
Regarding claim 5:
Takeuchi in view of Fletcher and Chandran and Chalkidis discloses all limitations of claim 4.
Takeuchi in view of Fletcher and Chandran and Chalkidis does not further disclose, the second number-of-target estimation unit is configured to select a model of supervised learning in which the number of targets estimated by the first number-of-target estimation unit is included as an input label.
Bender, from the same field of endeavor, teaches selecting a model of supervised learning in which the number of targets estimated by the first number-of-target estimation unit is included as an input label (providing supervised learning in performing people counting by employing labeled training data which can be input into a pattern recognition [0078]).
Therefore, it would have been obvious to one of ordinary skill in the art at the time before the claimed invention was filed to select a model of supervised learning in which the number of targets estimated by the first number-of-target estimation unit is included as an input label; thus eliminating ambiguity and allowing the model to reach estimated certainty.
Regarding claim 12:
Claim 12 is rejected for substantially same reason as applied to claim 5 above, except that claim 12 is in a method claim format.
Regarding claim 19:
Claim 19 is rejected for substantially same reason as applied to claim 5 above, except that claim 19 is in a non-transitory computer-readable storage medium format.
Claims 6, 13, and 20 are rejected under 35 U.S.C. 103(a) as being unpatentable over Takeuchi in view of Fletcher and Chandran and Chalkidis, as applied to claims 4, 11, and 18 above, respectively, and further in view of Tateno (US 2011/0238605 A1) hereinafter referred to as Tateno.
Regarding claim 6:
Takeuchi in view of Fletcher and Chandran and Chalkidis discloses all limitations of claim 4.
Takeuchi in view of Fletcher does not further disclose, while Chandran further teaches models to be used for the estimation of the machine learning are a plurality of independent models trained (modeling crowd estimation techniques # 1, #2, #3 to estimate crowd count [0005]).
Furthermore, Chalkidis states inputting a data set group in which numbers of labels included are different from one another (using sets of features in training dataset as input and labels[0098]).
Takeuchi in view of Fletcher and Chandran and Chalkidis does not further disclose the second number-of-target estimation unit is configured to further use certainty on the result of the estimation of the first number-of-target estimation unit as input information from the first number-of-target estimation unit, and select a model with a smaller number of labels included in the data set as the certainty on the result of the estimation of the first number-of-target estimation unit is higher.
Tateno, from the same field of endeavor, teaches using certainty on the result of the estimation of the first number-of-target estimation unit as input information from the first number-of-target estimation unit (certainty factor is inputted [0063], and select a model with a smaller number of labels included in the data set as the certainty on the result of the estimation of the first number-of-target estimation unit is higher (setting certainty factor higher if number of label to be assigned is smaller [0057]).
Therefore, it would have been obvious to one of ordinary skill in the art at the time before the claimed invention was filed to use certainty as input and select a model with a smaller number of labels included in the data set as the certainty on the result of the estimation of the first number-of-target estimation unit is higher; thus improving the prediction accuracy [0090].
Regarding claim 13:
Claim 13 is rejected for substantially same reason as applied to claim 6 above, except that claim 13 is in a method claim format.
Regarding claim 20:
Claim 20 is rejected for substantially same reason as applied to claim 6 above, except that claim 20 is in a non-transitory computer-readable storage medium format.
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure.
Afifi (US-20220303716-A1 [0024, 0046, 0047, 0065US-20200196091-A1]),
Jeong ( US-20200196091-A1 [0009, 0039]),
Liu (US-20160315682-A1 [0076, 0134]),
are all cited to show that selecting models to be used for the estimation of the number of targets using the machine learning mode, based on results derived from other estimation units, e.g., number of targets based on radio information or image, and propagation path information – would improve the accuracy of targets estimation in a variety of crowd conditions and crowd locations -- similar to the claimed invention.
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/C.Q.T/
/AYMAN A ABAZA/ Primary Examiner, Art Unit 2465
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