Ex Parte Banavar et alDownload PDFPatent Trial and Appeal BoardFeb 18, 201412114346 (P.T.A.B. Feb. 18, 2014) Copy Citation UNITED STATES PATENT AND TRADEMARKOFFICE UNITED STATES DEPARTMENT OF COMMERCE United States Patent and Trademark Office Address: COMMISSIONER FOR PATENTS P.O. Box 1450 Alexandria, Virginia 22313-1450 www.uspto.gov APPLICATION NO. FILING DATE FIRST NAMED INVENTOR ATTORNEY DOCKET NO. CONFIRMATION NO. 12/114,346 05/02/2008 Guruduth Somasekhara Banavar YOR920040011US2 2887 48063 7590 02/18/2014 RYAN, MASON & LEWIS, LLP 48 South Service Road Suite 100 Melville, NY 11747 EXAMINER KANG, IRENE S ART UNIT PAPER NUMBER 3695 MAIL DATE DELIVERY MODE 02/18/2014 PAPER Please find below and/or attached an Office communication concerning this application or proceeding. The time period for reply, if any, is set in the attached communication. PTOL-90A (Rev. 04/07) UNITED STATES PATENT AND TRADEMARK OFFICE 1 ___________ 2 3 BEFORE THE PATENT TRIAL AND APPEAL BOARD 4 ___________ 5 6 Ex parte GURUDUTH SOMASEKHARA BANAVAR, 7 JOHN SIDNEY DAVIS II, MARIA RENE EBLING, 8 and DABY MOUSSE SOW 9 ___________ 10 11 Appeal 2011-011814 12 Application 12/114,346 13 Technology Center 3600 14 ___________ 15 16 17 Before ANTON W. FETTING, MEREDITH C. PETRAVICK, and 18 PHILIP J. HOFFMANN, Administrative Patent Judges. 19 20 FETTING, Administrative Patent Judge. 21 22 23 DECISION ON APPEAL 24 Appeal 2011-011814 Application 12/114,346 2 STATEMENT OF THE CASE1 1 1 Our decision will make reference to the Appellants’ Appeal Brief (“App. Br.,” filed January 6, 2011) and Reply Brief (“Reply Br.,” filed May 31, 2011), and the Examiner’s Answer (“Ans.,” mailed March 29, 2011). Guruduth Somasekhara Banavar, John Sidney Davis II, Maria Rene 2 Ebling, and Daby Mousse Sow (Appellants) seek review under 3 35 U.S.C. § 134 of a final rejection of claims 1, 4, 6-20, and 23-26, the only 4 claims pending in the application on appeal. We have jurisdiction over the 5 appeal pursuant to 35 U.S.C. § 6(b). 6 The Appellants invented task management techniques based on user 7 context (Specification 1:9-10). 8 An understanding of the invention can be derived from a reading of 9 exemplary claim 1, which is reproduced below [bracketed matter and some 10 paragraphing added]. 11 1. A computer-based method 12 of scheduling at least one task 13 associated with at least one user, 14 comprising the steps of: 15 [1] obtaining context 16 associated with the at least one user; 17 [2] automatically determining, 18 by a processor of the computer, 19 a predicted user context 20 and 21 one or more predicted task attributes 22 associated with the at least one task 23 based at least in part on a statistical analysis 24 of at least a portion of the obtained user context; 25 and 26 [3] automatically determining, 27 by the processor of the computer, 28 a schedule 29 Appeal 2011-011814 Application 12/114,346 3 for the at least one user 1 to perform the at least one task 2 based on at least a portion of the predicted user 3 context 4 and 5 based on the one or more predicted task attributes 6 associated with the at least one task; 7 [4] wherein one of the one or more predicted task attributes 8 comprises 9 a task due date, 10 a level of task importance, 11 and 12 a task duration; 13 and 14 [5] wherein the step of determining the predicted user context 15 further comprises the steps of: 16 [6] identifying a pattern to be activated based on the 17 obtained user context; 18 [7] determining an action based on the identified pattern; 19 and 20 [8] determining the predicted user context based on the 21 action. 22 23 The Examiner relies upon the following prior art: 24 Powell US 2002/0065700 A1 May 30, 2002 Brand US 2003/0221915 A1 Dec. 4, 2003 Brain US 2004/0148178 A1 Jul. 29, 2004 Bucci US 6,823,315 B1 Nov. 23, 2004 Horvitz US 2007/0071209 A1 Mar. 29, 2007 25 Claims 1, 4, 6-13, 19, 20, 23, and 24 stand rejected under 35 U.S.C. 26 § 103(a) as unpatentable over Brain, Horvitz, and Powell. 27 Claims 15-18 stand rejected under 35 U.S.C. § 103(a) as unpatentable 28 over Horvitz and Powell. 29 Appeal 2011-011814 Application 12/114,346 4 Claim 14 stands rejected under 35 U.S.C. § 103(a) as unpatentable over 1 Brain, Brand, Horvitz, and Powell. 2 Claim 25 stands rejected under 35 U.S.C. § 103(a) as unpatentable over 3 Brain, Brand, Horvitz, Bucci, and Powell. 4 Claim 26 stands rejected under 35 U.S.C. § 103(a) as unpatentable over 5 Brain, Horvitz, Bucci, and Powell. 6 ISSUES 7 The issues of obviousness turn primarily on whether the art’s predictions 8 of task and resource outcomes imply predictions as to task attributes 9 resulting from those outcomes. 10 FACTS PERTINENT TO THE ISSUES 11 The following enumerated Findings of Fact (FF) are believed to be 12 supported by a preponderance of the evidence. 13 Facts Related to the Prior Art 14 Brain 15 01. Brain is directed to an expert management system for retail 16 service establishments. Brain para. [0002]. 17 02. The automated store management system receives information 18 from sensors and employees, processes the information, and 19 provides employees with workflow and task instructions. Thus, 20 the automated manager system employs human beings as sensors 21 and/or decision makers for select, pre-determined functions and/or 22 tasks. A variety of tasks can be performed by the system, 23 including, but not limited to, hiring/firing of employees, inventory 24 management and control, employee scheduling, building 25 maintenance scheduling, accounting and payroll, employee 26 Appeal 2011-011814 Application 12/114,346 5 performance tracking and management, such as performance 1 review, discipline and rewards, legal compliance, such as worker 2 time breaks. Other functions performed can include: 3 Accident/Injury Tracking, Applicant Flow, Applicant Tracking, 4 Attendance and Time Tracking, Corporate Property Tracking, Job 5 Advertisement, Salary Survey, Performance Review Tracking 6 with History, Contract/Temporary Employee and Volunteer 7 Tracking, and the like. Brain para. [0018]. 8 03. The workflow is divided into processes composed of tasks. The 9 system monitors these processes and their associated tasks. When 10 a process requires executing, the software determines the tasks to 11 be performed, then assigns the tasks to a Tasks List. Pending 12 tasks on the Tasks List are then evaluated by a Task List program, 13 which prioritizes the tasks and assigns an employee to a task. The 14 system then instructs the employee to perform the task at the 15 appropriate time. The system can also warn an employee that s/he 16 will need to perform a certain task shortly; thus, the employee can 17 prepare him/herself mentally to perform the task when the 18 computer instructs that the task now needs to be performed. Thus, 19 the system performs process and task monitoring and issues 20 commands for task execution. Brain para. [0021]. 21 04. Another logistical function performed by the system is staffing. 22 The system predicts demand for functions at a future date and 23 determines if staffing is adequate to perform the functions. If not, 24 the system requests additional staff for the future date. For 25 example, the system can communicate the future staff need to a 26 Appeal 2011-011814 Application 12/114,346 6 personnel recruiter. Conversely, if the system notes too many 1 employees for the workload, the system can terminate the 2 employment of selected employees. The system also schedules 3 employee attendance such that the establishment is adequately 4 staffed while minimizing employee hours and overtime. In case 5 of a temporary need for extra employees, the system can contact 6 an employee to request additional attendance or the system can 7 contact a temporary staffing agency. Thus, because the system 8 can anticipate the establishment's worker requirements, the system 9 can ask for reinforcements prior to actually needing them and can 10 send workers home when no longer needed. Brain para. [0025]. 11 Horvitz 12 05. Horvitz is directed to supporting collaboration and 13 communication by collecting data from one or more devices, and 14 learning predictive models that provide forecasts of users' 15 presence and availability. Horvitz provides information to people 16 or communication agents about the current or future status of a 17 user's presence and availability at multiple locations and/or the 18 user's access to one or more devices or channels of 19 communication. Horvitz para. [0002]. 20 06. Data is collected by considering user activity and proximity 21 from multiple devices, in addition to analysis of content of users' 22 calendars, time of day, and day of week, for example, wherein the 23 data is employed to construct one or more learning models to 24 forecast users' presence and availability. The present invention 25 facilitates real-time, peri-real time, and/or long-term planning for 26 Appeal 2011-011814 Application 12/114,346 7 messaging and collaboration by providing probabilistic 1 predictions about current and future states of users to authorized 2 persons and/or automated applications. Horvitz para. [0007]. 3 07. A Bayesian inference system is provided that supports 4 availability forecasting machinery and systems within the 5 framework of various automated applications. To build general 6 predictive models, data is collected or aggregated regarding a 7 user's activity and location from multiple sources, including data 8 about a user's activities on multiple devices in addition to data 9 from a calendar, for example. Also, forecasts can be generalized 10 with respect to presence and absence to other events of interest to 11 support collaboration and communication. Horvitz para. [0008]. 12 08. The predictive component on availability is utilized to estimate 13 when a user will likely be in a setting where he/she can or will 14 review messages deemed as urgent and received by a user's 15 system are answered with an adaptive out-of-office message, such 16 as when the message will likely be unseen for some amount of 17 time and/or the message is at least of some urgency, and/or is from 18 one or more people of particular importance to the user. Such 19 selective messages can be populated with dynamically computed 20 availability status, centering for example, on a forecast of how 21 long it will be until the user will likely review a message such as 22 an e-mail, or be available to review the message, or be in a 23 particular situation (e.g., "back in the office"). Other aspects can 24 include determining the time until a user will review different 25 kinds of information, based on review histories, and the time until 26 Appeal 2011-011814 Application 12/114,346 8 the user will be in one or more types of settings, each associated 1 with one or more types of feasible communications. Such 2 information can be transmitted to a message sender regarding the 3 user's ability or likelihood to engage in communications, or 4 respond within a given timeframe. Horvitz para. [0012]. 5 09. Horvitz can build and use models of a user's attentional focus 6 and workload as part of harnessing the role of interruptions on 7 users. These methods can reason about a user's workload from 8 observed events and, more specifically, infer the cost of 9 interruption to users associated with different kinds of alerting and 10 communications. Such models of interruption fuse together 11 information from multiple sensory channels, including desktop 12 events, analysis of calendar information, visual pose, and ambient 13 acoustical analyses, for example. Horvitz para. [0014]. 14 10. In order to generate the state information, the forecasting 15 service employs a learning component that can include one or 16 more learning models for reasoning about the user states. Such 17 models can include substantially any type of system such as 18 statistical/mathematical models and processes that include the use 19 of Bayesian learning, which can generate Bayesian dependency 20 models. Although elaborate reasoning models can be employed in 21 accordance with the present invention, it is to be appreciated that 22 other approaches can also utilized. For example, rather than a 23 more thorough probabilistic approach, deterministic assumptions 24 can also be employed. Thus, in addition to reasoning under 25 uncertainty, logical decisions can also be made regarding the 26 Appeal 2011-011814 Application 12/114,346 9 status, location, context, focus, and so forth of users and/or 1 associated devices. Horvitz para. [0057]. 2 11. In Horvitz’s real-time learning approach, rather than attempting 3 to build a large static predictive model for all possible queries, the 4 method focuses analysis by constructing a set of cases 240 from 5 the event database 224 that is consistent with a query 244 at hand. 6 This approach allows custom-tailoring of the formulation and 7 discretization of variables representing specific temporal 8 relationships among such landmarks as transitions between 9 periods of absence and presence and appointment start and end 10 times, as defined by the query 244. These cases 240 are fed to a 11 learning and inference subsystem 250, which constructs a 12 Bayesian network that is tailored for a target prediction 254. The 13 Bayesian network is used to build a cumulative distribution over 14 events of interest. In one aspect, the present invention employs a 15 learning tool to perform structure search over a space of 16 dependency models, guided by a Bayesian model score to identify 17 graphical models with the greatest ability to predict the data. 18 Horvitz para. [0071]. 19 12. The preference resolver examines the contactee preference data 20 and the contactor preference data to find correlations between the 21 two sets of data. In one example, information concerning the 22 correlations is stored in a resolved preference data. For group 23 communications, the preference resolver examines multiple sets of 24 preference data to find correlations between the preferences. By 25 way of illustration, for a communication between two parties, the 26 Appeal 2011-011814 Application 12/114,346 10 preference resolver can determine that both parties would prefer to 1 communicate by high priority email for communications 2 associated with a first task. Similarly, the preference resolver can 3 determine that the contactee would prefer to communicate by 4 collaborative editing and phone for communications concerning a 5 particular document, while the contactor would prefer to 6 communicate only by telephone. Thus, the preference resolver 7 produces data (e.g., resolved preference data) or initiates 8 processing that assigns values to the correlations between the 9 contactee preferences and the contactor preferences. In one 10 example aspect of the present invention, the preferences of the 11 contactee are given more weight, and thus, if the contactor 12 attempted a phone conversation concerning the document for 13 which the contactee preferred both phone and collaborative 14 editing, then the preference resolver produces data or initiates 15 processing that makes it more likely that the contactor 16 communicates by both phone and collaborative editing. In 17 another example aspect of the present invention, the preferences 18 of the contactor are given priority over the preferences of the 19 contactee. By way of illustration, when a human contactor is 20 attempting to communicate with an electronic contactee, the 21 preferences of the contactor are considered more important, and 22 thus the preference resolver produces values or initiates 23 processing that makes it more likely that the preferences of the 24 contactor are observed. Horvitz para. [0163]. 25 Appeal 2011-011814 Application 12/114,346 11 13. The context analyzer examines the contactee context data and 1 the contactor context data to find correlations between the two sets 2 of data. In one example of the present invention, information 3 concerning the correlations is stored in an analyzed context data. 4 For group communications, the context analyzer may examine 5 multiple sets of context data to extract information concerning the 6 contexts. Horvitz para. [0164]. 7 14. The context analyzer stores/analyzes information regarding 8 variables and parameters of a user that influence notification 9 decision-making. For example, the parameters may include 10 contextual information, such as the user's typical locations and 11 attentional focus or activities per the time of day and the day of 12 the week, and additional parameters conditioned on such 13 parameters, such as the devices users tend to have access to in 14 different locations. Such parameters may also be functions of 15 observations made autonomously via one or more sensors. 16 Horvitz para. [0173]. 17 15. Information stored by the context analyzer 3022, according to 18 one aspect of the present invention is inclusive of contextual 19 information determined by the analyzer. The contextual 20 information is determined by the analyzer 3022 by discerning the 21 user's location and attentional status based on one or more 22 contextual information sources (not shown), as is described in 23 more detail in a later section of the description. The context 24 analyzer 3022, for example, may be able to determine with 25 precision the actual location of the user via a global positioning 26 Appeal 2011-011814 Application 12/114,346 12 system (GPS) that is a part of a user's car or cell phone. The 1 analyzer may also employ a statistical model to determine the 2 likelihood that the user is in a given state of attention by 3 considering background assessments and/or observations gathered 4 through considering such information as the type of day, the time 5 of day, the data in the user's calendar, and observations about the 6 user's activity. The given state of attention can include whether 7 the user is open to receiving notification, busy and not open to 8 receiving notification, and can include other considerations such 9 as weekdays, weekends, holidays, and/or other occasions/periods. 10 Horvitz para. [0175]. 11 Powell 12 16. Powell is directed to efficiently scheduling service technicians 13 and resources to complete work assignments within a defined 14 geographic area. Powell para. [0001]. 15 17. Various scheduling methods exist to aid in and/or optimize such 16 scheduling of resources. One method of scheduling is the critical 17 path method, CPM, in which diagrams depict the stages of a 18 project as nodes, and the duration of the tasks required to reach 19 the successive stages as arrows. In a variation of CPM, known as 20 PERT, ranges of task duration may also be shown. Additional 21 information, such as cost or number of workers, may be added in 22 the form of text along the arrows or on the nodes of the diagram. 23 Powell paras. [0005]-[0006]. 24 18. Pooled work is a type of work that a mobile workforce will 25 perform that is not related to any particular customer or service 26 Appeal 2011-011814 Application 12/114,346 13 order. For a utility company, this work can include walking a gas 1 pipeline to check for leaks, climbing utility poles, and checking 2 cables and other company-owned hardware in the field. This 3 work can be categorized by priorities, and some of the lower 4 priority work is such that it can be performed anytime within a 5 specified period. As the deadline for a pooled work task 6 approach, the individual task may increase in priority. Powell 7 para. [0035]. 8 19. This scheduling and optimization engine may be defined as a 9 software object that performs scheduling and optimization 10 functions for a series of events within a database. The scheduling 11 and optimization engine itself is a powerful software object that 12 examines a database containing the scheduling data input and 13 rules. This software object can then create a schedule based on 14 both the data input and rules which it applies to this data. 15 Optimization routines are incorporated into the rules to create and 16 revise existing schedules in real time. The data input provided to 17 the engine includes work assignments, workforce abilities, 18 preferences, geographic locations, priorities, time windows and 19 the like. The rules instruct the engine how to sort and prioritize 20 different work assignments. Powell para. [0040]. 21 Brand 22 20. Brand is directed to optimizing group elevator scheduling. 23 Brand para. [0001]. 24 21. Brand says that Q-Learning is a known form of stochastic 25 optimal control. Brand para. [0019]. 26 Appeal 2011-011814 Application 12/114,346 14 ANALYSIS 1 We adopt the Examiner’s findings and analysis from Ans. 5-24 and 2 reach similar legal conclusions. 3 Claims 1, 4, 6-13, 19, 20, 23, and 24 rejected under 35 U.S.C. § 103(a) as 4 unpatentable over Brain, Horvitz, and Powell. 5 We are not persuaded by the Appellants’ argument that 6 . . . the combination of Brain, Horvitz and Powell fails to teach 7 or suggest the limitations of independent claims 1, 19 and 20 8 wherein one of one or more predicted task attributes comprises 9 a task due date, a level of task importance, and a task duration 10 (i.e., wherein one or more of the specific task attributes recited 11 in the claim are predicted). 12 . . . Appellants respectfully assert that the cited portion of 13 Horvitz does not teach or suggest predicting either a meeting's 14 date, its duration, or its importance to the user. Rather, the cited 15 portion of Horvitz teaches predicting the likelihood that a user 16 will attend a future meeting based on the meeting's date, 17 duration and role of the user. 18 . . . 19 . . . nowhere does Horvitz teach or suggest determining 20 an action based on an identified pattern and determining a 21 predicted user context based on the action. Brand and Powell 22 fail to supplement this deficiency of Horvitz. 23 App. Br. 11-12. 24 As to the predicted task attributes, Appellants argue that the art predicts the 25 drivers of the task attributes rather than the attributes themselves. This 26 argument ignores the simple reality that the attributes result from those 27 drivers. Thus, having predicted that a user will attend a meeting, Horvitz 28 necessarily predicts when the task of attending that meeting will occur and 29 how long it will take. Powell simply shows that such event timing and 30 duration are among the attributes that would be necessarily found, although 31 one of ordinary skill in task and project management knew that these 32 Appeal 2011-011814 Application 12/114,346 15 attributes are required to determine overall workload duration and resource 1 requirements. 2 As to the pattern-action-context flow, these words are not defined in the 3 claims or Specification, and merely describe data sets that are used to predict 4 some form of context, which is another word for data state. Thus, Horvitz 5 analyzes patterns of locations based on contexts of potential users who may 6 use those locations to determine actions that may occur at those locations 7 based on such patterns that then determine predicted user contexts such as 8 user presence at such locations bases on those actions. 9 As to claim 24, this simply assigns the level of task importance for a 10 given task based at least in part on one or more observations of whether the 11 user selects the given task over another task. As the Examiner found, 12 Horvitz does this with its preference resolver. The Appellants contend that 13 Horvitz “does not describe the manner in which this preference data is 14 obtained” (App. Br. 13), but neither does the claim. The claim merely 15 recites some basis in the outcome of a user having selected a task before 16 where other tasks may have been alternatives at the time as well. 17 Claims 15-18 rejected under 35 U.S.C. § 103(a) as unpatentable over 18 Horvitz and Powell. 19 Appellants’ arguments here are similar to those in support of claim 1 and 20 are equally unpersuasive. Again, Appellants argue that the art predicts 21 outcomes and ignoring that those outcomes necessarily imply user contexts, 22 tasks, and task attributes. 23 24 Appeal 2011-011814 Application 12/114,346 16 Claim 14 rejected under 35 U.S.C. § 103(a) as unpatentable over Brain, 1 Brand, Horvitz, and Powell. 2 We are not persuaded by the Appellants’ argument that Brand is non-3 analogous art and that Brand disparages Q-Learning. Claim 14 applies a Q-4 Learning algorithm to at least a portion of the user context. Brand says that 5 Q-Learning is a known form of stochastic optimal control. Thus, the 6 Examiner is not applying Brand to the remaining art, but applying Q-7 Learning as a known generic stochastic tool for optimizing control in 8 stochastic processes. Brand merely evidences this knowledge; there is 9 nothing particularly specific in Q-Learning tying it to Brand’s elevators. 10 Further, although Brand says that its process improves upon Q-Learning, 11 this does not detract from the known use of Q-Learning for such control 12 optimization. Appellants’ Specification at 11 does no more than similarly 13 present Q-Learning as an instance of an applicable algorithm, and cites no 14 particular advantage of Q-Learning as an algorithm per se. 15 Claim 25 rejected under 35 U.S.C. § 103(a) as unpatentable over Brain, 16 Brand, Horvitz, Bucci, and Powell. 17 We are not persuaded by the Appellants’ argument that Bucci does not 18 multiply availability. App. Br. 16. The manner of expressing availability is 19 not defined or even narrowed by the claim. Thus any indication of time is 20 also an indication of availability. As to the contention regarding 21 minimization of the objective, this is not recited in the claim.22 Appeal 2011-011814 Application 12/114,346 17 Claim 26 rejected under 35 U.S.C. § 103(a) as unpatentable over Brain, 1 Horvitz, Bucci, and Powell. 2 Appellants rely on the earlier arguments. 3 4 CONCLUSIONS OF LAW 5 The rejection of claims 1, 4, 6-13, 19, 20, 23, and 24 under 35 U.S.C. 6 § 103(a) as unpatentable over Brain, Horvitz, and Powell is proper. 7 The rejection of claims 15-18 under 35 U.S.C. § 103(a) as unpatentable 8 over Horvitz and Powell is proper. 9 The rejection of claim 14 under 35 U.S.C. § 103(a) as unpatentable over 10 Brain, Brand, Horvitz, and Powell is proper. 11 The rejection of claim 25 under 35 U.S.C. § 103(a) as unpatentable over 12 Brain, Brand, Horvitz, Bucci, and Powell is proper. 13 The rejection of claim 26 under 35 U.S.C. § 103(a) as unpatentable over 14 Brain, Horvitz, Bucci, and Powell is proper. 15 16 DECISION 17 The rejection of claims 1, 4, 6-20, and 23-26 is affirmed. 18 No time period for taking any subsequent action in connection with this 19 appeal may be extended under 37 C.F.R. § 1.136(a). See 37 C.F.R. 20 § 1.136(a)(1)(iv) (2011). 21 22 AFFIRMED 23 24 25 Klh 26 Copy with citationCopy as parenthetical citation