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
Continued Examination Under 37 CFR 1.114
A request for continued examination under 37 CFR 1.114, including the fee set forth in 37 CFR 1.17(e), was filed in this application after final rejection. Since this application is eligible for continued examination under 37 CFR 1.114, and the fee set forth in 37 CFR 1.17(e) has been timely paid, the finality of the previous Office action has been withdrawn pursuant to 37 CFR 1.114. Applicant's submission filed on 01/08/2026 has been entered.
Notice to Applicant
In response to the communication received on 01/08/2026, the following is a Non-Final Office Action for Application No. 18536886.
Status of Claims
Claims 1-7, 9-14, 18-21 and 23-24 are pending.
Claims 8, 15-17 and 22 are cancelled.
Claims 24 is new.
Response to Amendments
Applicant’s amendments have been fully considered.
Response to Arguments
Applicant’s arguments with respect to the claims have been considered but are moot in light of the new grounds of rejection, as necessitated by amendment.
As per the 101 rejection, Applicant argues that the claims are in favor of eligibility per Prong One of Step 2A, however Examiner respectfully disagrees. Per Prong One of Step 2A, the identified recitation of an abstract idea falls within at least one of the Abstract Idea Groupings consisting of: Mathematical Concepts, Mental Processes, or Certain Methods of Organizing Human Activity. Particularly, the identified recitation falls within the Mental Processes including concepts performed in the human mind (including an observation, evaluation judgment, opinion) and/or Certain Methods of Organizing Human Activity including managing personal behavior or relationships or interactions between people (including social activities, teaching, and following rules of instructions). Since the recitation of the claims falls into at least one of the above Groupings, there is a basis for providing further analysis with regard to Prong Two of Step 2A to determine whether the recitation of an abstract idea is deduced to being directed to an abstract idea. Thus, the rejection is maintained.
Applicant argues that the claims are in favor of eligibility per Prong Two of Step 2A, however Examiner respectfully disagrees. Per Prong Two of Step 2A, this judicial exception is not integrated into a practical application because the claim as a whole does not integrate the identified abstract idea into a practical application. The memory medium, processor, server and/or computer is recited at a high level of generality, i.e., as a generic processor performing a generic computer function of processing/transmitting data. This generic processor server limitation is no more than mere instructions to apply the exception using a generic computer component. Further, memory medium, processor, server and/or computer to inter alia perform the function of predicting a work time in the current work area is mere instruction to apply an exception using a generic computer component which cannot integrate a judicial exception into a practical application. Accordingly, this/these additional element(s) does/do not integrate the abstract idea into a practical application because it does not impose any meaningful limits on practicing the abstract idea. In other words, the present claims use a generic processing device and memory medium to inter alia perform the function of predicting a work time in the current work area which is a concept that can be performed in the human mind. The processor is merely used to perform the function(s), and the processor does not integrate the abstract idea into a practical application since there are no meaningful limits on practicing the abstract idea. Thus, since the claims are directed to the determined judicial exception in view of the two prongs of Step 2A, the 2019 PEG flowchart is directed to Step 2B. Thus, the rejection is maintained.
Applicant argues that the claims are in favor of eligibility per Step 2B, however Examiner respectfully disagrees. Therein, the additional elements and combinations therewith are examined in the claims to determine whether the claims as a whole amounts to significantly more than the judicial exception. It is noted here that the additional elements are to be considered both individually and as an ordered combination. In this case, the claims each at most comprise additional elements of: memory medium, processor, server and/or computer. Taken individually, the additional limitations each are generically recited and thus does not add significantly more to the respective limitations. Further, memory medium, processor, server and/or computer to inter alia perform the function of predicting a work time in the current work area is mere instruction to apply an exception using a generic computer component which cannot provide an inventive concept in Step 2B (or, looking back to Step 2A, cannot integrate a judicial exception into a practical application). For further support, the Applicant’s specification supports the claims being directed to use of a generic computer/memory type structure. Taken as an ordered combination, the claim(s) does/do not include additional elements that are sufficient to amount to significantly more than the judicial exception because the limitations are directed to limitations referenced in Alice Corp. that are not enough to qualify as significantly more when recited in a claim with an abstract idea include the non-limiting or non-exclusive examples of MPEP § 2106.05. Thus, the rejection is maintained.
In an effort to further expedite prosecution, see: Appendix 1 to the October 2019 Update: Subject Matter Eligibility, Life Sciences & Data Processing Examples, October 2019 30, Example 46. Livestock Management. Per claim 1 of Example 46, the memory, display and processor are recited so generically (no details whatsoever are provided other than that they are a memory, display and processor) that they represent no more than mere instructions to apply the judicial exception on a computer. These limitations can also be viewed as nothing more than an attempt to generally link the use of the judicial exception to the technological environment of a computer. As an exemplary direction for similar claim limitations to be eligible, see claims 2-4 of Example 46.
Priority
As required by M.P.E.P. 201.14(c), acknowledgement is made of applicant’s claim for priority based on: 18536886 filed 12/12/2023 is a Continuation of PCT/JP2021/022666, filed 06/15/2021.
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 1-7, 9-14, 18-21 and 23-24 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. The claims fall within statutory class of process or machine or manufacture; hence, the claims fall under statutory category of Step 1.
Step 2 is the two-part analysis from Alice Corp. (also called the Mayo test). The 2019 PEG makes two changes in Step 2A: It sets forth new procedure for Step 2A (called “revised Step 2A”) under which a claim is not “directed to” a judicial exception unless the claim satisfies a two-prong inquiry. The two-prong inquiry is as follows: Prong One: evaluate whether the claim recites a judicial exception (an abstract idea enumerated in the 2019 PEG, a law of nature, or a natural phenomenon). If claim recites an exception, then Prong Two: evaluate whether the claim recites additional elements that integrate the exception into a practical application of the exception. The claim(s) recite(s) the following abstract idea indicated by non-boldface font and additional limitations indicated by boldface font:
1. A work time prediction device, comprising: one or more processors; anda memory that stores instructions that, when executed by the one or more processors, cause the one or more processors to perform: an acquisition unit configured acquire area information regarding a current work area; and a prediction unit configured predict a work time in the current work area on the basis of the area information acquired by the acquisition unit and history information in which area information regarding a past work area and time information regarding a work time in the past work area are associated with each other wherein the area information includes size information regarding a size of the work area and division information regarding a division of work,wherein the predicting extracts past data similar to a current target division, the past data including work division, a size of the work area, a value of coefficient, and actual work time,predicts the work time in the current work area on the basis of the size of the current work area, the value of coefficient, and the past data,wherein the coefficient includes coefficient regarding at least one of an inclination, a waterside, an object, and a plant.
[or]
18. A work time prediction method, comprising:acquiring area information regarding a current work area; and predicting a work time in the current work area on the basis of the area information acquired in the acquiring and history information in which area information regarding a past work area and time information regarding a work time in the past work area are associated with each other, wherein the area information includes size information regarding a size of the work area and division information regarding a division of work,wherein the predicting extracts past data similar to a current target division, the past data including work division, a size of the work area, a value of coefficient, and actual work time,predicts the work time in the current work area on the basis of the size of the current work area, the value of coefficient, and the past data,wherein the coefficient includes coefficient regarding at least one of an inclination, a waterside, an object, and a plant.
[or]
19. A non-transitory computer readable storage medium storing a program for causing a computer to function as:an acquisition unit configured acquire area information regarding a current work area; and a prediction unit configured predict a work time in the current work area on the basis of the area information acquired by the acquisition unit and history information in which area information regarding a past work area and time information regarding a work time in the past work area are associated with each other wherein the area information includes size information regarding a size of the work area and division information regarding a division of work,wherein the prediction unit extracts past data similar to a current target division, the past data including work division, a size of the work area, a value of coefficient, and actual work time,predicts the work time in the current work area on the basis of the size of the current work area, the value of coefficient, and the past data,wherein the coefficient includes coefficient regarding at least one of an inclination, a waterside, an object, and a plant.
[or]
20. A non-transitory computer readable storage medium storing a program for causing a computer of a terminal device capable of communicating with a server device to execute a work prediction time display method, wherein the server device includes one or more processors; anda memory that stores instructions that, when executed by the one or more processors, cause the one or more processors to perform:coefficient includes coefficient regarding at least one of an inclination, a waterside, an object, and a plant andthe display method includes transmitting the area information regarding the current work area to the server device,receiving a prediction result of the prediction unit from the server device, anddisplaying the prediction result received in the receiving.
Per Prong One of Step 2A, the identified recitation of an abstract idea falls within at least one of the Abstract Idea Groupings consisting of: Mathematical Concepts, Mental Processes, or Certain Methods of Organizing Human Activity. Particularly, the identified recitation falls within the Mental Processes including concepts performed in the human mind (including an observation, evaluation judgment, opinion) and/or Certain Methods of Organizing Human Activity including managing personal behavior or relationships or interactions between people (including social activities, teaching, and following rules of instructions).
Per Prong Two of Step 2A, this judicial exception is not integrated into a practical application because the claim as a whole does not integrate the identified abstract idea into a practical application. The memory medium, processor, server and/or computer is recited at a high level of generality, i.e., as a generic processor performing a generic computer function of processing/transmitting data. This generic memory medium, processor, server and/or computer limitation is no more than mere instructions to apply the exception using a generic computer component. Further, predict a work time in the current work area on the basis of the area information and/or displaying the prediction result received in the receiving by a memory medium, processor, server and/or computer is mere instruction to apply an exception using a generic computer component which cannot integrate a judicial exception into a practical application. Accordingly, this/these additional element(s) does/do not integrate the abstract idea into a practical application because it does not impose any meaningful limits on practicing the abstract idea. Thus, since the claims are directed to the determined judicial exception in view of the two prongs of Step 2A, the 2019 PEG flowchart is directed to Step 2B.
Per Step 2B, the additional elements and combinations therewith are examined in the claims to determine whether the claims as a whole amounts to significantly more than the judicial exception. It is noted here that the additional elements are to be considered both individually and as an ordered combination. In this case, the claims each at most comprise additional elements of: memory medium, processor, server and computer. Taken individually, the additional limitations each are generically recited and thus does not add significantly more to the respective limitations. Further, predict a work time in the current work area on the basis of the area information and/or displaying the prediction result received in the receiving by a memory medium, processor, server and/or computer is mere instruction to apply an exception using a generic computer component which cannot provide an inventive concept in Step 2B (or, looking back to Step 2A, cannot integrate a judicial exception into a practical application). For further support, the Applicant’s specification supports the claims being directed to use of a generic computer/memory type structure at ¶0064 wherein “The processing unit 301 is a processor represented by a CPU, and executes a program stored in the storage unit 302 to implement various functions related to a terminal device 2.” Taken as an ordered combination, the claim(s) does/do not include additional elements that are sufficient to amount to significantly more than the judicial exception because the limitations are directed to limitations referenced in Alice Corp. that are not enough to qualify as significantly more when recited in a claim with an abstract idea include, as a non-limiting or non-exclusive examples: i. Adding the words "apply it" (or an equivalent) with the judicial exception, or mere instructions to implement an abstract idea on a computer, e.g., a limitation indicating that a particular function such as creating and maintaining electronic records is performed by a computer, as discussed in Alice Corp., 134 S. Ct. at 2360, 110 USPQ2d at 1984 (see MPEP § 2106.05(f));
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ii. Simply appending well-understood, routine, conventional activities previously known to the industry, specified at a high level of generality, to the judicial exception, e.g., a claim to an abstract idea requiring no more than a generic computer to perform generic computer functions that are well-understood, routine and conventional activities previously known to the industry, as discussed in Alice Corp., 134 S. Ct. at 2359-60, 110 USPQ2d at 1984 (see MPEP § 2106.05(d));
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iii. Adding insignificant extra-solution activity to the judicial exception, e.g., mere data gathering in conjunction with a law of nature or abstract idea such as a step of obtaining information about credit card transactions so that the information can be analyzed by an abstract mental process, as discussed in CyberSource v. Retail Decisions, Inc., 654 F.3d 1366, 1375, 99 USPQ2d 1690, 1694 (Fed. Cir. 2011) (see MPEP § 2106.05(g)); or
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v. Generally linking the use of the judicial exception to a particular technological environment or field of use, e.g., a claim describing how the abstract idea of hedging could be used in the commodities and energy markets, as discussed in Bilski v. Kappos, 561 U.S. 593, 595, 95 USPQ2d 1001, 1010 (2010) or a claim limiting the use of a mathematical formula to the petrochemical and oil-refining fields, as discussed in Parker v. Flook. The courts have recognized the following computer functions inter alia to be well-understood, routine, and conventional functions when they are claimed in a merely generic manner: performing repetitive calculations; receiving, processing, and storing data (e.g., the present claims); electronically scanning or extracting data; electronic recordkeeping; automating mental tasks (e.g., process/machine/manufacture for performing the present claims); and receiving or transmitting data (e.g., the present claims).
The dependent claims do not cure the above stated deficiencies, and in particular, the dependent claims further narrow the abstract idea without reciting additional elements that integrate the exception into a practical application of the exception or providing significantly more than the abstract idea. Dependent claims 15 and 17 provide a server to the limitations which is an additional element. However, server additional element is mere instruction to apply an exception using a generic computer component which cannot integrate a judicial exception into a practical application. Accordingly, this additional element does not integrate the abstract idea into a practical application because it does not impose any meaningful limits on practicing the abstract idea. Since there are no elements or ordered combination of elements that amount to significantly more than the judicial exception, the claims are not eligible subject matter under 35 USC §101.
Thus, viewed as a whole, these additional claim element(s) do not provide meaningful limitation(s) to transform the abstract idea into a patent eligible application of the abstract idea such that the claim(s) amounts to significantly more than the abstract idea itself. Therefore, the claim(s) are rejected under 35 U.S.C. 101 as being directed to non-statutory subject matter.
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 of this title, 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.
Claims 1-7, 9-14, 18-21 and 23-24 are rejected under 35 U.S.C. 103 as being unpatentable over Ebrahimi Afrouzi et al. (US 20220066456 A1) hereinafter referred to as Ebrahimi Afrouzi in view of Cui et al. (US 20150161555 A1) hereinafter referred to as Cui in further view of Srinivasan et al. (US 20050004805 A1) hereinafter referred to as Srinivasan.
Ebrahimi Afrouzi teaches:
Claim 1. A work time prediction device, comprising:
one or more processors; anda memory that stores instructions that, when executed by the one or more processors, cause the one or more processors to perform (¶0251 In some embodiments, a server used by a system of the robot may have a queue. For example, a compute core may be compared to an ATM machine with people lining up to use the ATM machine in turns. There may be two, three, or more ATM machines. This concept is similar to a server queue. In embodiments, T.sub.1 may be a time from a startup of a system to arrival of a first job. T.sub.2 may be a time between the arrival of the first job and an arrival of the second job and so on while S.sub.i (i.e., service time) may be a time each job needs of the core to perform the job itself.):
acquiring area information regarding a current work area (¶0300 In some embodiments, the processor uses a Markov chain to initialize a state n of the robot with an arbitrary value to overcome the dependence between localization and mapping as the machine moves in a state space or work area. In following time steps, the processor randomly updates x repeatedly and it converges to a fair sample from the distribution p(x). In some embodiments, the processor determines the transition distribution T(x′|x), when the chain transforms from a random state x to a state x′); and
predicting a work time in the current work area on the basis of the area information acquired by the acquisition unit and history information in which area information regarding a past work area and time information regarding a work time in the past work area are associated with each other, wherein the area information includes size information regarding a size of the work area and division information regarding a division of work,wherein the predicting extracts past data similar to a current target division, the past data including work division, a size of the work area, a value of coefficient, and actual work time, predicts the work time in the current work area on the basis of the size of the current work area, the value of coefficient, and the past data (¶0252 In embodiments, the core has the capacity to process a certain number of instructions per second. In some embodiments, W.sub.i is the waiting time of job i, wherein W.sub.i=max{W.sub.i−1, +S.sub.i−1−T.sub.i, 0}. Since the first job arrives when there is no queue, W.sub.1=0. For job i, the waiting time depends on how long job i−1 takes. If job i arrives after job i−1 ends, then W.sub.i=0. In contrast, if job i arrives before the end of job i−1, the waiting time of W.sub.i is the amount of time remaining to finish job i−1. ¶0705 In some embodiments, the user may use the user interface of the application to instruct the robot to begin performing work (immediately. In some embodiments, the application displays a battery level or charging status of the robot. In some embodiments, the amount of time left until full charge or a charge required to complete the remaining of a work session may be displayed to the user using the application. In some embodiments, the amount of work by the robot a remaining battery level can provide may be displayed. In some embodiments, the amount of time remaining to finish a task may be displayed. ¶0699 Any number of other parameters may be used without departing from embodiments disclosed herein, which is not to suggest that other descriptions are limiting. A service schedule may indicate the day and, in some cases, the time to service an area. For example, the robot may be set to service a particular area on Wednesday at noon. In some instances, the schedule may be set to repeat. A service frequency may indicate how often an area is to be serviced. In embodiments, service frequency parameters may include hourly frequency, daily frequency, weekly frequency, and default frequency. A service frequency parameter may be useful when an area is frequently used or, conversely, when an area is lightly used. ¶0285 In some embodiments, a neural network algorithm of a feed forward system may include a composite of multiple logistic regression. In such embodiments, the feed forward system may be a network in a graph including nodes and links connecting the nodes organized in a hierarchy of layers. In some embodiments, nodes in the same layer may not be connected to one other. In embodiments, there may be a high number of layers in the network (i.e., deep network) or there may be a low number of layers (i.e., shallow network). In embodiments, the output layer may be the final logistic regression that receives a set of previous logistic regression outputs as an input and combines them into a result),
wherein the coefficient includes coefficient regarding at least one of an inclination, a waterside, an object, and a plant (¶00073 A distance coefficient, T.sub.d (X, Y)=−log.sub.2(T.sub.s(X, Y)), based on the similarity ratio may also be used for bitmaps with non-zero similarity. Other similarity or dissimilarity measures may be used, such as RBF kernel in machine learning ¶0898 an object captured in an image, the object having high range of intensity, can be separated from a background having low range of intensity by thresholding wherein all pixel intensities below a certain threshold are discarded or segmented, leaving only the pixels of interest. In some embodiments, a metric can be used to indicate how good of an overlap there is between the two sets of perceived depths. For example, the Szymkiewicz-Simpson coefficient may be determine by the processor by dividing the number of overlapping readings between two overlapping sets of data, X and Y, by the number of readings of the smallest of the two data sets ¶1363 a Barker code may be a finite sequence of N values a of +1 and −1. In some embodiments, values a.sub.j for j=1, 2, . . . , N may have off-peak autocorrelation coefficients c.sub.v=Σ.sub.j=1.sup.n−v a.sub.ja.sub.j+v. In some embodiments, the autocorrelation coefficients are as small as possible, wherein |c.sub.v|≤1 for all 1≤v<N. In embodiments, sequences may be chosen for their spectral properties and low cross correlation with other sequences that may interfere).
Although not explicitly taught by Ebrahimi Afrouzi, Cui teaches in the analogous art of scheduling tasks to operators:
predicting a work time in the current work area on the basis of the area information acquired by the acquisition unit and history information in which area information regarding a past work area and time information regarding a work time in the past work area are associated with each other (¶0005 Computer-implemented methods, computer systems, and computer program products assign of tasks to operators for completion during a work period. In one embodiment, the system receives a set of pending tasks and a set of operators for performing tasks in a work period, for example, a shift. The system predicts an amount of time required by each operator to complete the task using a model trained with historical data describing tasks previously completed by members of the set of operators. The system determines a mapping from a subset of the pending tasks to operators such that each task is associated with an operator for performing the task during the work period. Each task may be associated with a value and the subset is selected to maximize the total value of the subset of tasks, using the predicted completion times from the various operators. The system assigns the tasks to the associated operators for completion during the work period and stores information describing the assigned tasks and the associated operators…In some embodiments, each model may be trained using historical data specific to a particular operator whereas in other embodiments, a model may be trained using historical data describing a set of operators, for example, a model representing a category of operators or single model representing the entire set of operators. In an embodiment, each task comprises fixing a defect associated with a business listing displayable on a map. The value of each task can be determined based on various factors, for example, urgency of the task, a click through rate of a web page associated with the business listing of the task, the number of times the defect specified in the task was reported, the density of population of the location of the business associated with the task, or a rate at which the business listing is requested by users ¶0024 The value can be a figure of merit, and represent any combination of factors, such as an amount of time to fix the defect, a monetary value associated with fixing the defect, a measure of importance in fixing the defect, a measure of priority for fixing the defect, the age of the defect, the source of the defect, the location of the defect, and so forth. A period of time can be a shift or a day during which operators work. In an embodiment, the task scheduler 110 predicts the amount of time needed by each operator to complete a task based on models developed using machine learning techniques.)
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to combine the scheduling tasks to operators of Cui with the system for obstacle recognition method for autonomous robots of Ebrahimi Afrouzi for the following reasons:
(1) a finding that there was some teaching, suggestion, or motivation, either in the references themselves or in the knowledge generally available to one of ordinary skill in the art, to modify the reference or to combine reference teachings, e.g. Ebrahimi Afrouzi ¶0004 teaches that autonomous or semi-autonomous robotic devices are increasingly used within consumer homes and commercial establishments;
(2) a finding that there was reasonable expectation of success since the only difference between the claimed invention and the prior art being the lack of actual combination of the elements in a single prior art reference, e.g. Ebrahimi Afrouzi Abstract teaches a robot, including: capturing images of a workspace; capturing movement data indicative of movement of the robot; capturing LIDAR data as the robot performs work within the workspace;…generating a first iteration of a map of the workspace based on the LIDAR data; generating additional iterations of the map based on newly captured LIDAR data and newly captured movement data; actuating the robot to drive along a trajectory that follows along a planned path by providing pulses to one or more electric motors of wheels of the robot, and Cui Abstract teaches a model that is generated for predicting the time taken by operators for completing a given task; and
(3) whatever additional findings based on the Graham factual inquiries may be necessary, in view of the facts of the case under consideration, to explain a conclusion of obviousness, e.g. Ebrahimi Afrouzi at least the above cited paragraphs, and Cui at least the inclusively cited paragraphs.
Therefore, it would be obvious to one skilled in the art at the time of the invention to combine the scheduling tasks to operators of Cui with the system for obstacle recognition method for autonomous robots of Ebrahimi Afrouzi. The rationale to support a conclusion that the claim would have been obvious is that "a person of ordinary skill in the art would have been motivated to combine the prior art to achieve the claimed invention and whether there would have been a reasonable expectation of success in doing so." DyStar Textilfarben GmbH & Co. Deutschland KG v. C.H. Patrick Co., 464 F.3d 1356, 1360, 80 USPQ2d 1641, 1645 (Fed. Cir. 2006). See MPEP 2143(G).
Although not explicitly taught by Ebrahimi Afrouzi in view of Cui, Srinivasan teaches in the analogous art of system of suggestive analysis of customer data:
wherein the area information includes size information regarding a size of the work area and division information regarding a division of work,wherein the predicting extracts past data similar to a current target division, the past data including work division, a size of the work area, a value of coefficient, and actual work time, predicts the work time in the current work area on the basis of the size of the current work area, the value of coefficient, and the past data, wherein the coefficient includes coefficient regarding at least one of an inclination, a waterside, an object, and a plant (¶0032 The customer is allowed to select one or more of the featured services such as load and unload labor, driving assistance, clean-up, landscaping, childcare, and repairs. The proposed job is forwarded to the actual service provider who may accept or reject the job or propose alternate conditions. The moving help website may also include hyperlinks to one or more websites that offer the featured services. ¶0038 From the size of the truck and/or the number and type of rooms, the moving help website can estimate and recommend the number of helpers which would most efficiently accomplish the moving task. The moving help website can provide a cost benefit analysis of time to do the job versus rate per hour of various service providers by using the customer and moving related information to estimate the job and then matching the job with service provider capabilities and options. With the customer's authorization, the moving related information, or a portion thereof, is also forwarded to the service providers to better estimate special or custom jobs. Each service provider will advertise a capability and have established a track record and feedback history from other customers. For example, from prior history, the moving help website knows that a 24-foot truck takes on the average 5 man-hours to load and another 5 man-hours to unload. The moving help website can offer alternatives in that a first moving help service provider may advertise that they can load and unload a 24-foot truck in 4.3 hours for a certain rate per hour while a second moving help service provider may offer a much lower price per hour but require more hours to do the job. From the feedback history, the first moving help service provider may have a high rating in terms of completing the job as promised with high quality and customer service marks. The second moving help service provider may consistently take more time than estimated and may have a history of customer complaints or damage to the moved items.¶0042 From the size of the truck and/or the number and type of rooms, the moving help website can recommend, or link the customer to another website than can recommend, houses or apartments in the destination location. The moving help website can pre-screen the available apartments and homes for floor plans that are consistent with the rooms of furniture to be moved, within a given budget and desirable neighborhood. If the customer is moving a home with 3 bedrooms, 2 bath, kitchen, living room, family room, and 2-car garage, then the customer will need a home or apartment that will fit the moved items. If the customer is only looking for temporary housing, e.g. a smaller 2-bedroom apartment until they have time to look for a home, then the moving help website will recommend the proper size storage unit which, in combination with the apartment, will fit the moved item ¶0046 The moving related information is also useful to (1) estimate the time needed to clean the old place and arrange for maid services, (2) arrange for daycare for children or the elderly, (3) determine optimum route, road closures, weather, and calendar of events of locations in route, (4) reserve hotels in route, (5) suggest restaurants, gas stations, and points of interest, (6) aid a realtor in finding a new home for the customer to purchase, (7) arrange for utilities in new place, and (8) arrange for lawn and landscaping services.).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to combine the system of suggestive analysis of customer data of Srinivasan with the system for obstacle recognition method for autonomous robots of Ebrahimi Afrouzi in view of Cui for the following reasons:
(1) a finding that there was some teaching, suggestion, or motivation, either in the references themselves or in the knowledge generally available to one of ordinary skill in the art, to modify the reference or to combine reference teachings, e.g. Ebrahimi Afrouzi ¶0004 teaches that autonomous or semi-autonomous robotic devices are increasingly used within consumer homes and commercial establishments;
(2) a finding that there was reasonable expectation of success since the only difference between the claimed invention and the prior art being the lack of actual combination of the elements in a single prior art reference, e.g. Ebrahimi Afrouzi Abstract teaches a robot, including: capturing images of a workspace; capturing movement data indicative of movement of the robot; capturing LIDAR data as the robot performs work within the workspace;…generating a first iteration of a map of the workspace based on the LIDAR data; generating additional iterations of the map based on newly captured LIDAR data and newly captured movement data; actuating the robot to drive along a trajectory that follows along a planned path by providing pulses to one or more electric motors of wheels of the robot, and Cui Abstract teaches a model that is generated for predicting the time taken by operators for completing a given task and Srinivasan Abstract teaches a website that provides information and recommendations generated from a suggestive analysis of the customer data for the benefit of the customer; and
(3) whatever additional findings based on the Graham factual inquiries may be necessary, in view of the facts of the case under consideration, to explain a conclusion of obviousness, e.g. Ebrahimi Afrouzi in view of Cui at least the above cited paragraphs, and Srinivasan at least the inclusively cited paragraphs.
Therefore, it would be obvious to one skilled in the art at the time of the invention to combine the system of suggestive analysis of customer data of Srinivasan with the system for obstacle recognition method for autonomous robots of Ebrahimi Afrouzi in view of Cui. The rationale to support a conclusion that the claim would have been obvious is that "a person of ordinary skill in the art would have been motivated to combine the prior art to achieve the claimed invention and whether there would have been a reasonable expectation of success in doing so." DyStar Textilfarben GmbH & Co. Deutschland KG v. C.H. Patrick Co., 464 F.3d 1356, 1360, 80 USPQ2d 1641, 1645 (Fed. Cir. 2006). See MPEP 2143(G).
Ebrahimi Afrouzi teaches:
Claim 2. The work time prediction device according to claim 1, wherein the area information further includes workability information regarding easiness of work in the work area (¶1162 In some embodiments, to optimize division of zones of an environment, the processor may proceed through the following iteration for each zone of a sequence of zones, beginning with the first zone: expansion of the zone if neighbor cells are empty, movement of the robot to a point in the zone closest to the current position of the robot, addition of a new zone coinciding with the travel path of the robot from its current position to a point in the zone closest to the robot if the length of travel from its current position is significant, execution of a coverage pattern (e.g. boustrophedon) within the zone, and removal of any uncovered cells from the zone… In some embodiments, the processor may determine optimal division of zones of an environment by modeling zones as emulsions of liquid, such as bubbles. In some embodiments, the processor may create zones of arbitrary shape but of similar size, avoid overlap of zones with static structures of the environment, and minimize surface area and travel distance between zones).
Ebrahimi Afrouzi teaches:
Claim 3. The work time prediction device according to claim 2, wherein the size information includes information regarding an area for each division of the work, and the workability information includes information regarding workability for each division of the work (¶1162 In some embodiments, to optimize division of zones of an environment, the processor may proceed through the following iteration for each zone of a sequence of zones, beginning with the first zone: expansion of the zone if neighbor cells are empty, movement of the robot to a point in the zone closest to the current position of the robot, addition of a new zone coinciding with the travel path of the robot from its current position to a point in the zone closest to the robot if the length of travel from its current position is significant, execution of a coverage pattern (e.g. boustrophedon) within the zone, and removal of any uncovered cells from the zone….In some embodiments, the processor may determine optimal division of zones of an environment by modeling zones as emulsions of liquid, such as bubbles. In some embodiments, the processor may create zones of arbitrary shape but of similar size, avoid overlap of zones with static structures of the environment, and minimize surface area and travel distance between zones).
Ebrahimi Afrouzi teaches:
Claim 4. The work time prediction device according to claim 1, wherein the acquiring acquires information input by a user as the area information (¶0858 FIG. 10 illustrates an example of a control system of a robot and components connected thereto. In some embodiments, the control system and related components are part of a robot and carried by the robot as the robot moves. Microcontroller unit (MCU) 800 of main printed circuit board (PCB) 801, or otherwise the control system or processor, has connected to it user interface module 802 to receive and respond to user inputs; bumper sensors 803, floor sensors 804, presence sensors 805 and perimeter and obstacle sensors).
Ebrahimi Afrouzi teaches:
Claim 5. The work time prediction device according to claim 1, wherein the acquiring acquires, as the area information, information based on a captured image of a device capable of capturing an image of the work area (¶0848 In some embodiments, a sensor of the robot (e.g., two-and-a-half dimensional LIDAR) observes the environment in layers. For example, FIG. 1A illustrates a robot 6400 taking sensor readings 6401 using a sensor, such as a two-and-a-half dimensional LIDAR. The sensor may observe the environment in layers. For example, FIG. 1B illustrates an example of a first layer 6402 observed by the sensor at a height 10 cm above the driving surface, a second layer 6403 at a height 40 cm above the driving surface, a third layer 6404 at a height 80 cm above the driving surface, a fourth layer 6405 at a height 120 cm above the driving surface, and a fifth layer 6406 at a height 140 cm from the driving surface, corresponding with the five measurement lines in FIG. 1A. In some embodiments, the processor of the robot determines an imputation of the layers in between those observed by the sensor based on the set of layers S={layer 1, layer 2, layer 3, . . . } observed by the sensor. In some embodiments, the processor may generate a set of layers 5′={layer 1′, layer 2′, layer 3′, . . . } in between the layers observed by the sensor, wherein layer 1′, layer 2′, layer 3′ may correspond with layers that are located a predetermined height above layer 1, layer 2, layer 3, respectively.).
Ebrahimi Afrouzi teaches:
Claim 6. The work time prediction device according to claim 1, wherein the predicting extracts the history information of the past work area similar to the current work area, and predicts the work time in the current work area on the basis of the extracted history information (¶0558 the discarded images may be archived or used for historical analysis and extracting structure from history. If, however, based on displacement or speed, the rate of quality images captured is not high enough, lower quality images and/or features may be used to compensate. Sometimes a CNN may be used to increase resolution of two consecutive images in an image stream by extracting features and creating a correspondence matrix).
Although not explicitly taught by Ebrahimi Afrouzi, Cui teaches in the analogous art of scheduling tasks to operators:
extracts the history information of the past work area similar to the current work area, and predicts the work time in the current work area on the basis of the extracted history information (¶0005 Computer-implemented methods, computer systems, and computer program products assign of tasks to operators for completion during a work period. In one embodiment, the system receives a set of pending tasks and a set of operators for performing tasks in a work period, for example, a shift. The system predicts an amount of time required by each operator to complete the task using a model trained with historical data describing tasks previously completed by members of the set of operators. The system determines a mapping from a subset of the pending tasks to operators such that each task is associated with an operator for performing the task during the work period. Each task may be associated with a value and the subset is selected to maximize the total value of the subset of tasks, using the predicted completion times from the various operators. The system assigns the tasks to the associated operators for completion during the work period and stores information describing the assigned tasks and the associated operators…In some embodiments, each model may be trained using historical data specific to a particular operator whereas in other embodiments, a model may be trained using historical data describing a set of operators, for example, a model representing a category of operators or single model representing the entire set of operators. In an embodiment, each task comprises fixing a defect associated with a business listing displayable on a map. The value of each task can be determined based on various factors, for example, urgency of the task, a click through rate of a web page associated with the business listing of the task, the number of times the defect specified in the task was reported, the density of population of the location of the business associated with the task, or a rate at which the business listing is requested by users ¶0024 The value can be a figure of merit, and represent any combination of factors, such as an amount of time to fix the defect, a monetary value associated with fixing the defect, a measure of importance in fixing the defect, a measure of priority for fixing the defect, the age of the defect, the source of the defect, the location of the defect, and so forth. A period of time can be a shift or a day during which operators work. In an embodiment, the task scheduler 110 predicts the amount of time needed by each operator to complete a task based on models developed using machine learning techniques. ¶0044 The feature extractor 230 may extract information from various sources including the map defect store 315 and the history log 320. Some features are extracted once for each map defect and are stored in the feature store 345, for example, characteristics of a business listing associated with the defect. Other features may be periodically extracted and updated, for example, features that change over time. A feature may change over time, for example, if an operator updates, adds, or deletes an attribute value. The various features of the defect that are extracted are used as inputs for machine learning).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to combine the scheduling tasks to operators of Cui with the system for obstacle recognition method for autonomous robots of Ebrahimi Afrouzi for the following reasons:
(1) a finding that there was some teaching, suggestion, or motivation, either in the references themselves or in the knowledge generally available to one of ordinary skill in the art, to modify the reference or to combine reference teachings, e.g. Ebrahimi Afrouzi ¶0004 teaches that autonomous or semi-autonomous robotic devices are increasingly used within consumer homes and commercial establishments;
(2) a finding that there was reasonable expectation of success since the only difference between the claimed invention and the prior art being the lack of actual combination of the elements in a single prior art reference, e.g. Ebrahimi Afrouzi Abstract teaches a robot, including: capturing images of a workspace; capturing movement data indicative of movement of the robot; capturing LIDAR data as the robot performs work within the workspace;…generating a first iteration of a map of the workspace based on the LIDAR data; generating additional iterations of the map based on newly captured LIDAR data and newly captured movement data; actuating the robot to drive along a trajectory that follows along a planned path by providing pulses to one or more electric motors of wheels of the robot, and Cui Abstract teaches a model that is generated for predicting the time taken by operators for completing a given task; and
(3) whatever additional findings based on the Graham factual inquiries may be necessary, in view of the facts of the case under consideration, to explain a conclusion of obviousness, e.g. Ebrahimi Afrouzi at least the above cited paragraphs, and Cui at least the inclusively cited paragraphs.
Therefore, it would be obvious to one skilled in the art at the time of the invention to combine the scheduling tasks to operators of Cui with the system for obstacle recognition method for autonomous robots of Ebrahimi Afrouzi. The rationale to support a conclusion that the claim would have been obvious is that "a person of ordinary skill in the art would have been motivated to combine the prior art to achieve the claimed invention and whether there would have been a reasonable expectation of success in doing so." DyStar Textilfarben GmbH & Co. Deutschland KG v. C.H. Patrick Co., 464 F.3d 1356, 1360, 80 USPQ2d 1641, 1645 (Fed. Cir. 2006). See MPEP 2143(G).
Ebrahimi Afrouzi teaches:
Claim 7. The work time prediction device according to claim 1, wherein the predicting acquires a prediction result of the work time by performing input according to the area information acquired by the acquisition unit to a learned model that performs machine learning using the history information as teacher data (¶0699 Any number of other parameters may be used without departing from embodiments disclosed herein, which is not to suggest that other descriptions are limiting. A service schedule may indicate the day and, in some cases, the time to service an area. For example, the robot may be set to service a particular area on Wednesday at noon. In some instances, the schedule may be set to repeat. A service frequency may indicate how often an area is to be serviced. In embodiments, service frequency parameters may include hourly frequency, daily frequency, weekly frequency, and default frequency. A service frequency parameter may be useful when an area is frequently used or, conversely, when an area is lightly used.).
Ebrahimi Afrouzi teaches:
Claim 9. The work time prediction device according to claim 1, the one or more processors are further caused to perform: outputting it configured output an estimation of a work cost based on the work time predicted by the prediction unit (¶0570 In some embodiments, the robot may initially enter a patrol mode wherein the robot observes the environment and generates a spatial representation of the environment. In some embodiments, the processor of the robot may use a cost function to minimize the length of the path of the robot required to generate the complete spatial representation of the environment. In some embodiments, a path of the robot is generated using a cost function to minimize the length of the path of the robot required to generate a complete spatial representation.).
Ebrahimi Afrouzi teaches:
Claim 10. The work time prediction device according to claim 9, wherein the outputting outputs an estimation of the work cost of a plurality of patterns according to the number of working machines used in a planned work (¶0570 In some embodiments, the robot may initially enter a patrol mode wherein the robot observes the environment and generates a spatial representation of the environment. In some embodiments, the processor of the robot may use a cost function to minimize the length of the path of the robot required to generate the complete spatial representation of the environment. In some embodiments, a path of the robot is generated using a cost function to minimize the length of the path of the robot required to generate a complete spatial representation. ¶0621 the processor may relate the location of the plant and the position of the robot using a cost function and minimize the cost function to narrow down a region around the mean. The results of minimizing the cost function is a reduction in the uncertainty in the locations of the plant and robot. ).
Ebrahimi Afrouzi teaches:
Claim 11. The work time prediction device according to claim 1, further comprising:a storage unit configured store the history information (¶1402 the system of the robot may push its status to the cloud and the application may pull the status from the cloud. The application may also push a command to the cloud which may be pulled by system of the robot, and in response, enacted. The cloud may also store and forward data. For instance, the system of the robot may constantly or incrementally push or pull map, trajectory, and historical data. In some cases, the application may push a data request. The data request may be retrieved by the system of the robot, and in response, the system of the robot may push the requested data to the cloud. ).
Ebrahimi Afrouzi teaches:
Claim 12. The work time prediction device according to claim 2, wherein the workability information includes information regarding at least one selected from the group consisting of an inclination of the work area, a non-entry region of a working machine in the work area, an object disposed in the work area, and a type of a plant in the work area (¶0940 the processor of the robot may adjust the plane of reference each time a new lower point is discovered and all vertical measurements accordingly. In some embodiments, the plane of reference may be positioned at a height of the work surface at a location where the robot begins to perform work and data may be assigned a positive value when an area with an increased height relative to the plane of reference is discovered (e.g., an inclination or bump) and assigned a negative value when an area with a decreased height relative to the plane of reference is observed.).
Ebrahimi Afrouzi teaches:
Claim 13. The work time prediction device according to claim 2, wherein the size information includes information regarding a surface area of a hedge to be a work target (¶0691 the application may identify one or more unlikely perimeter segments by detecting one or more perimeter segments oriented at an unusual angle (e.g., less than 25 degrees relative to a neighboring segment or some other threshold) or one or more perimeter segments comprising an unlikely contour of a perimeter (e.g., short perimeter segments connected in a zig-zag form). In some embodiments, the application may identify an unlikely perimeter segment by determining the surface area enclosed by three or more connected perimeter segments, one being the newly created perimeter segment and may identify the perimeter segment as an unlikely perimeter segment if the surface area is less than a predetermined (or dynamically determined) threshold. ¶0793 in a tiled floor, where the UV is applied downward, the robot may pause for 30 minutes or 60 minutes on a certain time to move on to the next tile. In some embodiments, the speed of the robot when using the UV is adjustable depending on the application. For example, the robot may clean a particular surface area (e.g., hospital floor tile or house kitchen tile or another surface area) for a particular amount of time (e.g., 60 minutes or 30 minutes or another time) to eliminate a particular percentage of bacteria (e.g., 100% or 50% or another percentage). In some embodiments, the amount of time spent cleaning a particular surface area depends on any of: the percentage of elimination of bacteria desired, the type of bacteria, the half-life of bacteria for the UV light used (e.g., UVC light) and its strength, and the application.).
Ebrahimi Afrouzi teaches:
Claim 14. The work time prediction device according to claim 3, wherein the division of the work includes at least one selected from the group consisting of lawn work, grass work, manual work, and hedge work (¶1276 devices of user and devices available to the public (e.g., smart gas pump, robotic lawn mower, or service robot) may be connected in an integrated system. In some embodiments, the user may request usage or service of an unowned device and, in some cases, the user may pay for the usage or service. In some cases, payment is pay as you go. For example, a user may request a robotic lawn mower to mow their lawn every Saturday. The CAIT system may manage the request, deployment of a robotic lawn mower to the home of the user, and payment for the service. ¶1404 In some embodiments, the user may use the application to manually control the robot. For example, FIG. 253H illustrates buttons 6409 for moving the robot forward, 6410 for moving the robot backwards, 6411 for rotating the robot clockwise, 6412 for rotating the robot counterclockwise, 6413 for toggling robot between autonomous and manual mode (when in autonomous mode play symbol turns into pause symbol), 6414 for summoning the robot to the user based on, for example, GPS location of the user's phone, and 6415 for instructing the robot to go to a particular area of the environment.).
As per claims 18,19, and 20, the method, manufacture, and manufacture tracks the device of claims 1, 1, and 1&17, respectively, resulting in substantially similar limitations. The same cited prior art and rationale of claims 1, 1, and 1&17 are applied to claims 18,19, and 20, respectively. Ebrahimi Afrouzi discloses that the embodiment may be found as a system and manufacture (Fig. 1 and ¶0838).
Ebrahimi Afrouzi teaches:
Claim 21. The work time prediction device according to claim 1, wherein the area information includes division information regarding a division of work,the arithmetic expression is a regression expression obtained by obtained by single regression analysis or multiple regression analysis using values of the size of each division and each coefficient which is multiplied by the size included in the history information as an explanatory variable and using the work time as an objective variable (¶0238 The processor may, for example, receive and process data from internal or external sensors, execute commands based on data received, control motors such as wheel motors, map the environment, localize the robot, determine division of the environment into zones, and determine movement paths. In some cases, the robot may include a microcontroller on which computer code required for executing the methods and techniques described herein may be stored ¶1162 In some embodiments, to optimize division of zones of an environment, the processor may proceed through the following iteration for each zone of a sequence of zones, beginning with the first zone: expansion of the zone if neighbor cells are empty, movement of the robot to a point in the zone closest to the current position of the robot, addition of a new zone coinciding with the travel path of the robot from its current position to a point in the zone closest to the robot if the length of travel from its current position is significant, execution of a coverage pattern (e.g. boustrophedon) within the zone, and removal of any uncovered cells from the zone. ¶1163 In some embodiments, the processor may determine optimal division of zones of an environment by modeling zones as emulsions of liquid, such as bubbles. In some embodiments, the processor may create zones of arbitrary shape but of similar size, avoid overlap of zones with static structures of the environment, and minimize surface area and travel distance between zones).
Although not explicitly taught by Ebrahimi Afrouzi, Cui teaches in the analogous art of scheduling tasks to operators:
the arithmetic expression is a regression expression obtained by obtained by single regression analysis or multiple regression analysis using values of the size of each division and each coefficient which is multiplied by the size included in the history information as an explanatory variable and using the work time as an objective variable (¶0038 The training module 355 obtains training data from the history logs 320 and stores the training data in the training data store 330. The training data store 330 stores training sets comprising tuples, each tuple including information identifying an operator, a map defect, and the time taken by the operator to fix the map defect. The training module 355 trains a machine learning model for each operator or for groups of operators sharing similar attributes using the data from training data store 330. The resulting operator model 360 predicts the amount of time taken to fix a given defect by an operator. The operator model 360 can be a least square regression model. In other embodiments, the operator model can be a tree-based model, kernel method, neural network, or ensemble of one or more of these techniques. ¶0053 an operator model 360 trained using the training data predicts a large completion time for that particular type of task, so that tasks of that particular type are not likely to be assigned to the operator by the task scheduler. ¶0054 The training module 355 generates a machine learning model for each operator based on the operator's respective data set. Based on the training, operator models 360a, 360b, 360c are generated for the operators 170a, 170b, and 170c respectively. Each operator model 360 predicts the expected completion time for a given defect by the corresponding operator. For example, given a defect 410, operator model 360a predicts the expected time taken by operator 170a to fix the defect 410, operator model 360b predicts the expected time taken by operator 170b to fix the defect 410, and operator model 360c predicts the expected time taken by operator 170c to fix the defect 410. In general, an operator model 360 can be developed to predict expected completion time for any type of task, for example, quality testing, trouble shooting, or delivery of certain item.).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to combine the scheduling tasks to operators of Cui with the system for obstacle recognition method for autonomous robots of Ebrahimi Afrouzi for the following reasons:
(1) a finding that there was some teaching, suggestion, or motivation, either in the references themselves or in the knowledge generally available to one of ordinary skill in the art, to modify the reference or to combine reference teachings, e.g. Ebrahimi Afrouzi ¶0004 teaches that autonomous or semi-autonomous robotic devices are increasingly used within consumer homes and commercial establishments;
(2) a finding that there was reasonable expectation of success since the only difference between the claimed invention and the prior art being the lack of actual combination of the elements in a single prior art reference, e.g. Ebrahimi Afrouzi Abstract teaches a robot, including: capturing images of a workspace; capturing movement data indicative of movement of the robot; capturing LIDAR data as the robot performs work within the workspace;…generating a first iteration of a map of the workspace based on the LIDAR data; generating additional iterations of the map based on newly captured LIDAR data and newly captured movement data; actuating the robot to drive along a trajectory that follows along a planned path by providing pulses to one or more electric motors of wheels of the robot, and Cui Abstract teaches a model that is generated for predicting the time taken by operators for completing a given task; and
(3) whatever additional findings based on the Graham factual inquiries may be necessary, in view of the facts of the case under consideration, to explain a conclusion of obviousness, e.g. Ebrahimi Afrouzi at least the above cited paragraphs, and Cui at least the inclusively cited paragraphs.
Therefore, it would be obvious to one skilled in the art at the time of the invention to combine the scheduling tasks to operators of Cui with the system for obstacle recognition method for autonomous robots of Ebrahimi Afrouzi. The rationale to support a conclusion that the claim would have been obvious is that "a person of ordinary skill in the art would have been motivated to combine the prior art to achieve the claimed invention and whether there would have been a reasonable expectation of success in doing so." DyStar Textilfarben GmbH & Co. Deutschland KG v. C.H. Patrick Co., 464 F.3d 1356, 1360, 80 USPQ2d 1641, 1645 (Fed. Cir. 2006). See MPEP 2143(G).
Ebrahimi Afrouzi teaches:
Claim 23. The work time prediction device according to claim 1, further comprising:an input unit configured to receive a user input of the values of the coefficients (¶1363 a Barker code may be a finite sequence of N values a of +1 and −1. In some embodiments, values a.sub.j for j=1, 2, . . . , N may have off-peak autocorrelation coefficients c.sub.v=Σ.sub.j=1.sup.n−v a.sub.ja.sub.j+v. In some embodiments, the autocorrelation coefficients are as small as possible, wherein |c.sub.v|≤1 for all 1≤v<N. In embodiments, sequences may be chosen for their spectral properties and low cross correlation with other sequences that may interfere ¶1403 User inputs are sent from the GUI to the robot for implementation. For example, the user may use the application to create boundary zones or virtual barriers and cleaning areas. FIG. 244 illustrates an example of a user using an application of a communication device to create a rectangular boundary zone 5500 (or a cleaning area, for example) by touching the screen and dragging a corner 5501 of the rectangle 5500 in a particular direction to change the size of the boundary zone 5500).
Ebrahimi Afrouzi teaches:
Claim 24. The work time prediction device according to the work time prediction device according to wherein the predicting predicts a work time required for work in the work area in which the work is planned using an arithmetic expression based on the history information,wherein the arithmetic expression is a regression expression obtained by regression analysis using values that are based on the history information as an explanatory variable and an objective variable (¶0038 The training module 355 obtains training data from the history logs 320 and stores the training data in the training data store 330. The training data store 330 stores training sets comprising tuples, each tuple including information identifying an operator, a map defect, and the time taken by the operator to fix the map defect. The training module 355 trains a machine learning model for each operator or for groups of operators sharing similar attributes using the data from training data store 330. The resulting operator model 360 predicts the amount of time taken to fix a given defect by an operator. The operator model 360 can be a least square regression model. In other embodiments, the operator model can be a tree-based model, kernel method, neural network, or ensemble of one or more of these techniques. ¶0053 an operator model 360 trained using the training data predicts a large completion time for that particular type of task, so that tasks of that particular type are not likely to be assigned to the operator by the task scheduler. ¶0054 The training module 355 generates a machine learning model for each operator based on the operator's respective data set. Based on the training, operator models 360a, 360b, 360c are generated for the operators 170a, 170b, and 170c respectively. Each operator model 360 predicts the expected completion time for a given defect by the corresponding operator. For example, given a defect 410, operator model 360a predicts the expected time taken by operator 170a to fix the defect 410, operator model 360b predicts the expected time taken by operator 170b to fix the defect 410, and operator model 360c predicts the expected time taken by operator 170c to fix the defect 410. In general, an operator model 360 can be developed to predict expected completion time for any type of task, for example, quality testing, trouble shooting, or delivery of certain item).
Conclusion
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/KURTIS GILLS/Primary Examiner, Art Unit 3624