CTNF 18/692,020 CTNF 89187 DETAILED ACTION 07-03-aia AIA 15-10-aia The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA. This Action is non-final and is in response to the claims filed March 14, 2024 via preliminary amendment. Claims 1, 15-28, 30-33, and 35 are currently pending, of which claims 1, 15, 16, 18, 19, 21-25, 27, 31-33, and 35 are currently amended. Claims 2-14, 29, and 34 have been cancelled. Specification 06-11 AIA The title of the invention is not descriptive. A new title is required that is clearly indicative of the invention to which the claims are directed. Claim Objections 07-29-01 AIA Claim s 17, 21, 25, and 33 are objected to because of the following informalities: Claim 17 recites numerous parts of “the task”. There appears to be a grammatical/typographical issue as the penultimate item in the list (“quality assurance of automated devices”) is not followed by an “and” or an “or” (or an “and/or”). Claim 21 recites numerous parameters of the sensors. There appears to be a grammatical/typographical issue as the penultimate item in the list (“filtering”) is not followed by an “and” or an “or” (or an “and/or”). Claim 25 recites “input resolution; layer depth; layer width”. There appears to be a grammatical/typographical issue as the penultimate item in the list (“layer depth”) is not followed by an “and” or an “or” (or an “and/or”). Claim 31 recites numerous parts of “the sensors”. There appears to be a grammatical/typographical issue as the penultimate item in the list (“thermal imaging sensors”) is not followed by an “and” or an “or” (or an “and/or”) . Appropriate correction is required. Claim Rejections - 35 USC § 112 07-30-02 AIA 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. The following is a quotation of 35 U.S.C. 112 (pre-AIA), second paragraph: The specification shall conclude with one or more claims particularly pointing out and distinctly claiming the subject matter which the applicant regards as his invention. 07-34-01 Claim(s) 17 and 30 is/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 applications subject to pre-AIA 35 U.S.C. 112, the applicant), regards as the invention. Claim 17 recites a list of tasks. However, as discussed above, there is not any specific language that delineates the penultimate limitation of the claim from the final limitation of the claim. In this specific instance, there is a further issue because the claim is not clear whether this listing must include all of the listed tasks, or at least one of the listed tasks. If it is all of the tasks, this needs to be delineated with an “and” after the penultimate “quality assurance” limitation. If this is at least one of the tasks, this needs to be delineated with an “or” or an “and/or”. Claim 30 recites “the apparatus of claim 29” and claim 29 was cancelled and therefore it is unclear which prior claim that claim 30 should depend from. For examination purposes, it will be treated as depending from claim 16. Examiner’s Note The prior art rejections below cite particular paragraphs, columns, and/or line numbers in the references for the convenience of the applicant. Although the specified citations are representative of the teachings in the art and are applied to the specific limitations within the individual claim, other passages and figures may apply as well. It is respectfully requested that, in preparing responses, the applicant fully consider the references in their entirety as potentially teaching all or part of the claimed invention, as well as the context of the passage as taught by the prior art. 07-06 AIA 15-10-15 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. Claim Rejections - 35 USC § 103 07-20-aia AIA 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. 07-21-aia AIA Claim(s ) 1, 15-26, 30-33, and 35 is /are rejected under 35 U.S.C. 103 as being unpatentable over At hreya et al. (W.O. 2021/158225; hereinafter “Athreya”) and further in view of Park et al. (U.S. Publication No. 2021/0406560; hereinafter, “Park”). As per claim 1 , Athreya teaches a method of configuring a sensor grid system having a plurality of sensors arranged to collect data from a working space, the method comprising: applying an output from the sensors to a task model for performing a task associated with the working space (See Athreya Fig. 2 and paras. [0018], [0047-48] and [0068]: sensors for environment conditions, including their placement, that can be input into a selection model); determining a task accuracy parameter corresponding to the accuracy with which the task model performs the task (See Athreya paras. [0032-33]: target accuracy of model); and in response to the task accuracy parameter being below a task accuracy parameter threshold: increasing the complexity of the task model (See Athreya paras. [0045] and [0060]: increasing machine learning model structure complexity if below threshold target accuracy). However, while Athreya teaches increasing model complexity, Athreya does not explicitly teach increasing sensor resolution. Park teaches increasing the resolution of the output from the sensors (See Park para. [0008]: “To further increase the accuracy and precision of the multi-sensor fusion network, one or more additional channels may be provided as input to the multi-sensor fusion network”). It would have been obvious before the effective filing date of the claimed invention to a person having ordinary skill in the art to combine, with a reasonable expectation of success, the model complexity of Athreya with the additional sensors of Park. One would have been motivated to combine these references because both references disclose machine learning control systems with autonomous robots, and Park further enhances the model accuracy of Athreya because “[b]y reducing noise and increasing the accuracy and precision of the multi-sensor fusion network, the downstream processes that rely on these outputs of the multi-sensor fusion network—representing locations, velocities, poses, appearances, etc. of static and dynamic objects or features in the environment—may also benefit from increased performance and effectiveness” (See Park para. [0009]). As per claim 15 , Athreya/Park further teaches the method of claim 1, comprising using the resolution of the sensors and the complexity of the task model to perform the task using the data collected from the plurality of sensors (See Athreya paras. [0052-53]: based on inferencing results, can control vehicles and robots or perform other commands). As per claim 16 , the claim is directed to an apparatus that implements the same features of the method of claim 1 and is therefore rejected for at least the same reasons therein. Furthermore, Athreya teaches the apparatus comprising a processor and memory containing instructions executable by the processor to configure the apparatus to implement said method (See Athreya paras. [0054-55]). As per claim 17 , Athreya/Park further teaches the apparatus of claim 16, wherein the task comprises: detecting predetermined objects (See Athreya para. [0052]: object detection ) ; controlling automated devices (See Athreya par. [0062]: controlling robot and self-driving vehicles); quality assurance of automated devices (See Athreya para. [0062]: reporting on parts detected on an assembly line); identifying security threats (See Athreya para. [0062]: identify person in security camera footage). As per claim 18 , Athreya/Park teaches the apparatus of claim 16 . However, while Athreya teaches increasing the complexity of the model, Athrey does not explicitly teach repeating the increase of the sensor resolution. Park teaches operative to increase the resolution of the output from the sensors again, following increasing the complexity of the task model (See Park para. [0008]: “To further increase the accuracy and precision of the multi-sensor fusion network, one or more additional channels may be provided as input to the multi-sensor fusion network – e.g., at each iteration”. Therefore, the iteration will continue until the targeted accuracy of Athreya is reached) It would have been obvious before the effective filing date of the claimed invention to a person having ordinary skill in the art to combine Athreya with the teachings of Park for at least the same reasons as discussed above in claim 1. As per claim 19 , Athreya/Park teaches the apparatus of claim 16 . However, while Athreya teaches sensors, Athreya does not explicitly teach working space arrangements of the sensors. Park teaches wherein the sensors are arranged to monitor respective regions of the working space and the task comprises detecting predetermined objects (See Park paras. [0071-72] and [0088]: environmental map data and sensors arranged and can be overlapped to determine and update an occupancy grid). It would have been obvious before the effective filing date of the claimed invention to a person having ordinary skill in the art to combine Athreya with the teachings of Park for at least the same reasons as discussed above in claim 1. As per claim 20 , Athreya/Park further teaches the apparatus of claim 19, wherein the predetermined objects are persons and an output of the sensor grid is used to control automated devices within the working space (See Athreya para. [0007]: “navigation (e.g., navigating a robot or autonomous vehicle to a location while avoiding obstacles)”; para. [0052]: object detection as well, including person, that would affect control of a robot). As per claim 21 , Athreya/Park teaches the apparatus of claim 16 . However, while Athreya teaches increasing model complexity, Athreya does not explicitly teach increasing sensor resolution. Park teaches wherein the resolution of the output from the sensors is increased by tuning any one or more of the following parameters of the sensors: pixel resolution; sampling frequency; quantization; bandwidth; filtering; the number of sensors. (See Park para. [0008]: “To further increase the accuracy and precision of the multi-sensor fusion network, one or more additional channels may be provided as input to the multi-sensor fusion network”; para. [0049]: up-sampling spatial resolution of DNN layers). It would have been obvious before the effective filing date of the claimed invention to a person having ordinary skill in the art to combine Athreya with the teachings of Park for at least the same reasons as discussed above in claim 1. As per claim 22 , Athreya/Park further teaches the apparatus of claim 16, wherein the task model is a pre-trained neural network (See Athreya para. [0031]: “a machine learning model or models may be selected from a set of pre-trained machine learning models”. As per claim 23 , Athreya/Park further teaches the apparatus of claim 22, comprising a training module configured to generate the trained neural network by one or more of: training an untrained neural network using the output from the sensors; using transfer learning from another trained neural network trained using a different working space (See Athreya para. [0058]: pre-trained models vs machine learning trained by apparatus; see also Park para. [0185]: training can be performed by a variety of methods, includes transfer learning). It would have been obvious before the effective filing date of the claimed invention to a person having ordinary skill in the art to combine Athreya with the teachings of Park for at least the same reasons as discussed above in claim 1. As per claim 24 , Athreya/Park further teaches the apparatus of claim 22, wherein the memory contains a plurality of neural networks of different complexity (See Athreya paras. [0030] and [0032]: varying complexity of models based on sensor conditions. This includes image sensors associated with inference levels and some situations may require more complex inferencing when “the illumination condition is demanding”, for example). As per claim 25 , Athreya further teaches the apparatus of claim 22, wherein increasing the complexity of the task model comprises using another neural network having an increase in one or more of the following architectural parameters: input resolution; layer depth; layer width (See Athreya paras. [0042] and [0045]: “each layer of the machine learning model structure may have a separate quantization. The quantization for a layer may depend on a factor or factors, such as weight distribution, layer depth, etc.” Furthermore, apparatus may increase complexity and/or quantization in a case that the confidence value is below a target accuracy based on the error feedback) As per claim 26 , Athreya further teaches the apparatus of claim 20, wherein the task model is a neural network trained using the output from the sensors together with person sensing data from the automated devices (See Athreya paras. [0018] and [0061-62]: inferencing can include detected person presence data; para. [0032]: trained based on facial recognition images). As per claim 30 , Athreya further teaches the apparatus of claim 29, wherein the memory comprises the task model (See Athreya para. [0058]: memory may store machine learning model). As per claim 31 , the claim is directed to a system that implements the same features of the method of claim 1 and is therefore rejected for at least the same reasons therein. Furthermore, Athreya teaches a sensor grid having a plurality of sensors arranged to collect data from a working space and a task model, the sensors and the task model configured by an apparatus, the apparatus comprising a processor and memory containing instructions executable by the processor to configure the apparatus to implement said method (See Athreya paras. [0054-55]). As per claim 32 , Athreya/Park teaches the system of claim 31, wherein the apparatus is further configured to configure the sensor grid (See Athreya para. [0018]: sensor placement in the environment; see also Park paras. [0071-72] and [0088]: environmental map data and sensors arranged and can be overlapped to determine and update an occupancy grid). It would have been obvious before the effective filing date of the claimed invention to a person having ordinary skill in the art to combine Athreya with the teachings of Park for at least the same reasons as discussed above in claim 1. As per claim 33 , Athreya/Park further teaches the system of claim 31, wherein the sensors comprise one or more of the following: thermometers; thermal imaging sensors; cameras (See Athreya paras. [0020-22]: sensors such as cameras). As per claim 35 , the claim is directed to a storage media that implements the same features of the method of claim 1 and is therefore rejected for at least the same reasons therein. Furthermore, Athreya teaches a non-transitory computer readable storage media having stored thereon a computer program that, when executed, performs a method of configuring a sensor grid system having a plurality of sensors arranged to collect data from a working space, the method comprising said method pf claim 1 (See Athreya paras. [0054-55]) . 07-21-aia AIA Claim (s) 27 and 28 is/are rejected under 35 U.S.C. 103 as being unpatentable over Athreya as applied above, and further in view of Stroman et al. (U.S. Publication No. 2019/0107408; hereinafter “Stroman”) . As per claim 27 , Athreya further teaches t he apparatus of claim 20, wherein the task accuracy parameter threshold corresponds to a collision avoidance limit between persons and automated devices (See Athreya para. [0007]: “navigation (e.g., navigating a robot or autonomous vehicle to a location while avoiding obstacles)”; para. [0052]: object detection as well, including person, the would affect control of a robot). However, while Athreya teaches collision avoidance, Athreya does not explicitly teach minimizing energy use. Stroman teaches a requirement to minimize energy use by the sensors and task model (See Stroman abstract and paras. [0041-43]: energy consumption taken into account when plotting paths, including identifying the “minimum energy or minimum fuel consumption path, called the global path”). It would have been obvious before the effective filing date of the claimed invention to a person having ordinary skill in the art to combine, with a reasonable expectation of success, the vehicle and robotics controls of Athreya with the energy determinations of Stroman. One would have been motivated to combine these references because both references disclose vehicle obstacle avoidance, and Stroman further enhances the vehicle navigation of Athreya by ensuring efficient use of fuel and minimizing energy waste (See Stroman paras. [0008] and [0051]). As per claim 28 , Athreya/Park teaches the apparatus of claim 27. While Athreya increases the complexity in response to the task accuracy not being reached (See Athreya para. [0045]), Athreya does not do so with the number of sensors. Park teaches adding more sensors to the sensor grid in response to the task accuracy parameter threshold not being reached (See Park para. [0008]: “To further increase the accuracy and precision of the multi-sensor fusion network, one or more additional channels may be provided as input to the multi-sensor fusion network – e.g., at each iteration”. Therefore, the iteration will continue until the targeted accuracy of Athreya is reached) It would have been obvious before the effective filing date of the claimed invention to a person having ordinary skill in the art to combine Athreya with the teachings of Park for at least the same reasons as discussed above in claim 1 . Conclusion 07-96 AIA The prior art made of record and not relied upon is considered pertinent to applicant's disclosure. Any inquiry concerning this communication or earlier communications from the examiner should be directed to Nicholas Klicos whose telephone number is (571)270-5889. The examiner can normally be reached Mon-Fri 9:00 AM-5:00 PM. 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, Scott Baderman can be reached at (571) 272-3644. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300. Information regarding the status of published or unpublished applications may be obtained from Patent Center. Unpublished application information in Patent Center is available to registered users. To file and manage patent submissions in Patent Center, visit: https://patentcenter.uspto.gov. Visit https://www.uspto.gov/patents/apply/patent-center for more information about Patent Center and https://www.uspto.gov/patents/docx for information about filing in DOCX format. For additional questions, contact the Electronic Business Center (EBC) at 866-217-9197 (toll-free). If you would like assistance from a USPTO Customer Service Representative, call 800-786-9199 (IN USA OR CANADA) or 571-272-1000. /NICHOLAS KLICOS/Primary Examiner, Art Unit 2118 Application/Control Number: 18/692,020 Page 2 Art Unit: 2118 Application/Control Number: 18/692,020 Page 3 Art Unit: 2118 Application/Control Number: 18/692,020 Page 4 Art Unit: 2118 Application/Control Number: 18/692,020 Page 5 Art Unit: 2118 Application/Control Number: 18/692,020 Page 6 Art Unit: 2118 Application/Control Number: 18/692,020 Page 9 Art Unit: 2118 Application/Control Number: 18/692,020 Page 10 Art Unit: 2118 Application/Control Number: 18/692,020 Page 13 Art Unit: 2118 Application/Control Number: 18/692,020 Page 14 Art Unit: 2118
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