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
1. This is a first non-final Office Action on the merits for application 19074136. Claims 1-20 are pending examination.
Double Patenting
2. 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.
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Claims 1-20 are rejected on the ground of nonstatutory double patenting as being unpatentable over Claim 1-10 of U.S. Patent No. 12,273,784. Although the claims at issue are not identical, they are not patentably distinct from each other because the claims are directed to the same subject matter, perform similar method steps and a person of ordinary skill in the art would not be free to practice one of the claimed inventions without infringing upon the other inventions.
Application number: 19074136
1. A non-transitory computer readable storage medium comprising stored instructions, the instructions when executed cause at least one processor to: train a neural network specific to a physical structure and configured to determine, based on weighted scores assigned to device parameters of a mobile device, whether the mobile device is physically located within boundaries associated with the physical structure; detect, via a plurality of wireless access points of the physical structure, a plurality of pings from a device; measure, by each wireless access point, a signal strength associated with each ping detected by the wireless access point; and determine whether the device is physically located within the boundaries associated with the physical structure by applying the neural network to the signal strengths associated with the plurality of pings measured by the plurality of wireless access points.
Patent number: 12,273,7841. A non-transitory computer readable storage medium comprising stored instructions, the instructions when executed cause at least one processor to:
generate a set of training data representative of devices previously located within boundaries associated with a physical structure and devices previously located outside the boundaries associated with the physical structure, the set of training data including characteristics of pings received from the devices previously located within the boundaries and outside the boundaries and including labels indicative of whether a device each ping was received from was located within the boundaries or outside the boundaries, wherein the pings received at times outside of hours of operation of the physical structure are labeled as located outside the boundaries;
train a neural network specific to the physical structure using the generated set of training data, wherein the neural network is configured to determine, based on weighted scores assigned to device parameters of a mobile device, whether the mobile device is physically located within the boundaries associated with the physical structure;
detect, via a plurality of wireless access points of the physical structure, a plurality of pings from a device;
measure, by each wireless access point, a signal strength associated with each ping detected by the wireless access point; and
determine whether the device is physically located within the boundaries associated with the physical structure by applying the neural network to the signal strengths associated with the plurality of pings measured by the plurality of wireless access points.
8. A method comprising: training a neural network specific to a physical structure and configured to determine,based on weighted scores assigned to device parameters of a mobile device, whether the mobile device is physically located within boundaries associated with the physical structure; detecting, via a plurality of wireless access points of the physical structure, a plurality of pings from a device; measuring, by each wireless access point, a signal strength associated with each ping detected by the wireless access point; and determining whether the device is physically located within the boundaries associated with the physical structure by applying the neural network to the signal strengths associated with the plurality of pings measured by the plurality of wireless access points.
10. A method comprising:
generating a set of training data representative of devices previously located within boundaries associated with a physical structure and device previously located outside the boundaries associated with the physical structure, the set of training data including characteristics of pings received from the devices previously located within the boundaries and outside the boundaries and including labels indicative of whether a device each ping was received from was located within the boundaries or outside the boundaries, wherein the pings received at times outside of hours of operation of the physical structure are labeled as located outside the boundaries;
training a neural network specific to the physical structure using the generated set of training data, wherein the neural network is configured to determine, based on weighted scores assigned to device parameters of a mobile device, whether the mobile device is physically located within the boundaries associated with the physical structure;
detecting, via a plurality of wireless access point of the physical structure, a plurality of pings from a device;
measuring, by each wireless access point, a signal strength associated with each ping detected by the wireless access point; and
determining whether the device is physically located within the boundaries associated with the physical structure by applying the neural network to the signal strengths associated with the plurality of pings measured by the plurality of wireless access points.
As to the independent claims:
Instant claim 2, and 9 is fully disclosed in claim 2, and 7 of the copending application number 12,273,784.
Instant claim 3, and 10 is fully disclosed in claim 3, and 8 of the copending application numbers 12,273,784.
Instant claim 4, and 11 is fully disclosed in claim 4, and 9 of the copending application numbers 12,273,784.
Instant claim 5, and 14 is fully disclosed in claim 7, and 10 of the copending application numbers 12,273,784.
It would have been obvious to one having ordinary skill in the art to make the changes above in order to cover slightly broader limitations. Furthermore, the claimed elements perform the same function as before.
Claim Rejections - 35 USC § 101
3. 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 1-20 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) 8 is/are drawn to method (i.e., a process), claim(s) 15 is/are drawn to a system (i.e., a machine/manufacture), and claim(s) 1 is/are drawn to non-transitory computer readable medium (i.e., a machine/manufacture). As such, claims 1, 8, and 15 is/are drawn to one of the statutory categories of invention.
Claims 1-20 are directed to generating training data based on device parameter values for a user device for locations visited. Specifically, claim(s) 1, 8, and 15 recite(s) train a specific to a physical structure and configured to determine, based on weighted scores assigned to parameters, whether physically located within boundaries associated with the physical structure; detect, the physical structure, a plurality of pings; measure, a signal strength associated with each ping detected; and determine whether the physically located within the boundaries associated with the physical structure by applying the signal strengths associated with the plurality of pings measured, which is grouped within the Methods Of Organizing Human Activity and is similar to the concept of (commercial or legal interactions including agreements in the form of contracts, legal obligations, advertising, marketing or sales activities or behaviors business relations) grouping of abstract ideas in prong one of step 2A of the Alice/Mayo test (See 2019 Revised Patent Subject Matter Eligibility Guidance, 84 Fed. Reg. 50, 52, 54 (January 7, 2019)). Accordingly, the claims recite an abstract idea (See pages 7, 10, Alice Corporation Pty. Ltd. v. CLS Bank International, et al., US Supreme Court, No. 13-298, June 19, 2014; 2019 Revised Patent Subject Matter Eligibility Guidance, 84 Fed. Reg. 50, 53-54 (January 7, 2019)).
The Claim limitations are listed under Methods Of Organizing Human Activity, and grouped as following:
determine, based on weighted scores assigned to parameters, whether physically located within boundaries associated with the physical structure; which is similar to the concept of (advertising, marketing or sales activities or behaviors business relations),
detect, the physical structure, a plurality of pings; which is similar to the concept of (advertising, marketing or sales activities or behaviors business relations),
measure, a signal strength associated with each ping detected; and determine whether the physically located within the boundaries associated with the physical structure by applying the signal strengths associated with the plurality of pings measured; which is similar to the concept of (advertising, marketing or sales activities or behaviors business relations).
This judicial exception is not integrated into a practical application because, when analyzed under prong two of step 2A of the Alice/Mayo test (See 2019 Revised Patent Subject Matter Eligibility Guidance, 84 Fed. Reg. 50, 54-55 (January 7, 2019)), the additional element(s) of the claim(s) such as non-transitory computer readable storage medium, processor, neural network, device, mobile device, wireless access points merely use(s) a computer as a tool to perform an abstract idea and/or generally link(s) the use of a judicial exception to a particular technological environment. Specifically, the non-transitory computer readable storage medium, processor, neural network, device, mobile device, wireless access points perform(s) the steps or functions of determine, based on weighted scores assigned to parameters, whether physically located within boundaries associated with the physical structure; detect, the physical structure, a plurality of pings; measure, a signal strength associated with each ping detected; and determine whether the physically located within the boundaries associated with the physical structure by applying the signal strengths associated with the plurality of pings measured. The use of a processor/computer as a tool to implement the abstract idea and/or generally linking the use of the abstract idea to a particular technological environment does not integrate the abstract idea into a practical application because it requires no more than a computer performing functions that correspond to acts required to carry out the abstract idea. The additional elements do not involve improvements to the functioning of a computer, or to any other technology or technical field (MPEP 2106.05(a)), the claims do not apply or use the abstract idea to effect a particular treatment or prophylaxis for a disease or medical condition (Vanda Memo), the claims do not apply the abstract idea with, or by use of, a particular machine (MPEP 2106.05(b)), the claims do not effect a transformation or reduction of a particular article to a different state or thing (MPEP 2106.05(c)), and the claims do not apply or use the abstract idea in some other meaningful way beyond generally linking the use of the abstract idea to a particular technological environment, such that the claim as a whole is more than a drafting effort designed to monopolize the exception (MPEP 2106.05(e) and Vanda Memo). Therefore, the claims do not, for example, purport to improve the functioning of a computer. Nor do they effect an improvement in any other technology or technical field. Accordingly, the additional elements do not impose any meaningful limits on practicing the abstract idea, and the claims are directed to an abstract idea.
The claim(s) does/do not include additional elements that are sufficient to amount to significantly more than the judicial exception because, when analyzed under step 2B of the Alice/Mayo test (See 2019 Revised Patent Subject Matter Eligibility Guidance, 84 Fed. Reg. 50, 52, 56 (January 7, 2019)), the additional element(s) of using a non-transitory computer readable storage medium, processor, neural network, device, mobile device, wireless access points to perform the steps amounts to no more than using a computer or processor to automate and/or implement the abstract idea of generating training data based on device parameter values for a user device for locations visited. As discussed above, taking the claim elements separately, the non-transitory computer readable storage medium, processor, neural network, device, mobile device, wireless access points perform(s) the steps or functions of determine, based on weighted scores assigned to parameters, whether physically located within boundaries associated with the physical structure; detect, the physical structure, a plurality of pings; measure, a signal strength associated with each ping detected; and determine whether the physically located within the boundaries associated with the physical structure by applying the signal strengths associated with the plurality of pings measured. These functions correspond to the actions required to perform the abstract idea. Viewed as a whole, the combination of elements recited in the claims merely recite the concept of generating training data based on device parameter values for a user device for locations visited. Therefore, the use of these additional elements does no more than employ the computer as a tool to automate and/or implement the abstract idea. The use of a computer or processor to merely automate and/or implement the abstract idea cannot provide significantly more than the abstract idea itself (MPEP 2106.05(I)(A)(f) & (h)). Therefore, the claim is not patent eligible.
As for dependent claims 2-7, 9-14, and 16-20 further describe the abstract idea of generating training data based on device parameter values for a user device for locations visited. Claim(s) 2-7, 9-14, and 16-20 does/do not include additional elements that are sufficient to amount to significantly more than the judicial exception because, when analyzed under step 2B of the Alice/Mayo test (See 2019 Revised Patent Subject Matter Eligibility Guidance, 84 Fed. Reg. 50, 52, 56 (January 7, 2019)), the additional element(s) of using a non-transitory computer readable storage medium, processor, neural network, device, mobile device, wireless access points to perform the steps amounts to no more than using a computer or processor to automate and/or implement the abstract idea of generating training data based on device parameter values for a user device for locations visited. As discussed above, taking the claim elements separately, the non-transitory computer readable storage medium, processor, neural network, device, mobile device, wireless access points perform(s) the steps or functions of a signal strength of a ping, a time of the ping, a dwell time between a first ping and a last ping, whether the ping was received during hours of operation of the physical structure, a signal strength of pings that connected to the wireless access point, and a manufacturer identifier of a MAC address; wherein is trained using training data captured inside of and outside of the physical structure; wherein further configured to differentiate located within the boundaries associated with the physical structure and outside the boundaries associated with the physical structure based on parameter values; wherein further configured to identify a location within the physical structure is located; wherein configured to produce a confidence score representative of a likelihood is located within the boundaries associated with the physical structure; identify a media access control (MAC) address; and determine, based on the MAC address, that a user had previously viewed an advertisement for the physical structure. These functions correspond to the actions required to perform the abstract idea. Viewed as a whole, the combination of elements recited in the claims merely recite the concept of generating training data based on device parameter values for a user device for locations visited. Therefore, the use of these additional elements does no more than employ the computer as a tool to automate and/or implement the abstract idea. The use of a computer or processor to merely automate and/or implement the abstract idea cannot provide significantly more than the abstract idea itself (MPEP 2106.05(I)(A)(f) & (h)). Therefore, the claim is not patent eligible.
Prior Art
4. In reference to independent claims 1, 8, and 15, the Office is unaware of any references that teach, individually or without an unreasonable combination of references, the combination of limitations steps found in the claims especially limitation that says: “measure, by each wireless access point, a signal strength associated with each ping detected by the wireless access point and determine whether the device is physically located within the boundaries associated with the physical structure by applying the neural network to the signal strengths associated with the plurality of pings measured by the plurality of wireless access points.”. No reference found that would teach the above limitation(s).
The first most relevant prior art identified by the Examiner is 20170006434. It teaches compares the subject sample to the historical samples to determine that the subject user is currently in or near a particular merchant's shop. the WPS receives a subject signal strength sample from the subject user's mobile device and compares these current “subject” samples to the historical sample data. the subject signal strength sample matches signal strengths of a particular historical sample, the WPS may provide the location associated with that historical sample as the position of the subject shopper within the venue, but it does not explicitly teaches training a machine learning model to measure, by each wireless access point, a signal strength associated with each ping detected by the wireless access point and determine whether the device is physically located within the boundaries associated with the physical structure by applying the neural network to the signal strengths associated with the plurality of pings measured by the plurality of wireless access points. Therefore, it lacks the combination of claimed elements as claimed by the independent claims.
The second most relevant prior art identified by the Examiner is/are 20210136514. It teaches using that machine learning model to determine a device location uses machine learning, but it is missing the feature of measure, by each wireless access point, a signal strength associated with each ping detected by the wireless access point and determine whether the device is physically located within the boundaries associated with the physical structure by applying the neural network to the signal strengths associated with the plurality of pings measured by the plurality of wireless access points. Therefore, it lacks the combination of claimed elements as claimed by the independent claims.
All these references listed above teaches some of the features in the limitations of the claim but when combining it becomes not obvious and the references would teach the claim as a whole.
Examiner note: none of the references or combined references teach the combination of limitations of claim 1, 8, and 15 or no reference found that would teaches the combination of limitations of claim 1, 8, and 15, especially claim limitations: determine whether the device is physically located within the boundaries associated with the physical structure by applying the neural network to the signal strengths associated with the plurality of pings measured by the plurality of wireless access points, and which is an idea of a model is configured to determine whether a device is located within a venue. During a baseline time period, the system detects wireless pings from mobile devices. The system obtains device parameters from the wireless pings. The system evaluates the device parameters to determine whether a mobile device entered the venue or remained outside of the venue. The system trains a model on training data corresponding to the baseline time period, the model configured to differentiate between devices that enter the venue and devices that remain outside the venue based on device parameters associated with the device. The system applies the model to future detected devices to determine whether or not the devices enter the venue.
When taken as a whole, the claims are not rendered obvious as the available prior art does not suggest or otherwise render obvious the noted features nor does the available prior art suggest or otherwise render obvious further modification of the evidence at hand. Such modifications would require substantial reconstruction relying solely on improper hindsight bias, and thus would not be obvious.
NPL Reference
5. The prior art made of record and not relied upon is considered pertinent to applicant's disclosure. The NPL “Deep Learning on Mobile Devices Through Neural Processing Units and Edge Computing” describes “Deep Neural Network (DNN) is becoming adopted for video analytics on mobile devices. To reduce the delay of running DNNs, many mobile devices are equipped with Neural Processing Units (NPU). However, due to the resource limitations of NPU, these DNNs have to be compressed to increase the processing speed at the cost of accuracy. To address the low accuracy problem, we propose a Confidence Based Offloading (CBO) framework for deep learning video analytics. The major challenge is to determine when to return the NPU classification result based on the confidence level of running the DNN, and when to offload the video frames to the server for further processing to increase the accuracy. We first identify the problem of using existing confidence scores to make offloading decisions, and propose confidence score calibration techniques to improve the performance. Then, we formulate the CBO problem where the goal is to maximize accuracy under some time constraint, and propose an adaptive solution that determines which frames to offload at what resolution based on the confidence score and the network condition. Through real implementations and extensive evaluations, we demonstrate that the proposed solution can significantly outperform other approaches.”.
Pertinent Art
6. The prior art made of record and not relied upon is considered pertinent to applicant's disclosure. Reference#20170316281 teaches similar invention which describes computing-based device 800 comprises one or more processors 802 which are microprocessors, controllers or any other suitable type of processors for processing computer executable instructions to control the operation of the device in order to compute adversarial examples and train a neural network image classifier using the adversarial examples. In some examples, for example where a system on a chip architecture is used, the processors 802 include one or more fixed function blocks (also referred to as accelerators) which implement a part of the method of any of FIGS. 5, 6, 7, and or a neural network image classifier 822 in hardware (rather than software or firmware). Platform software comprising an operating system 804 or any other suitable platform software is provided at the computing-based device to enable application software to be executed on the device. The application software comprises an adversarial example generator 810 in some examples. The application software comprises a training engine 806 in some examples, and a neural network 822 image classifier in some examples. A data store 808 at the computing device stores values of parameters used by the training engine 806, images, class labels, uncertainty data associated with class labels generated by the neural network, training data and other data. Once a robust neural network image classifier is formed using the training engine 806 it is sent to a target system 824 via a communications network in some cases.
Conclusion
Any inquiry concerning this communication or earlier communications from the examiner should be directed to TAREK ELCHANTI whose telephone number is (571) 272-9638. The examiner can normally be reached on Flex Mon - Thur 7-7:00 and Fri 7-4:00.
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/TAREK ELCHANTI/Primary Examiner, Art Unit 3621B
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