DETAILED CORRESPONDENCE
This action is in response to the filing of the Amendments on 02/04/2026. Claims 13, 20 and 21 are cancelled.
Notice of Pre-AIA or AIA Status
The present application, filed on or after March 16, 2013, is being examined
under the first inventor to file provisions of the AIA .
Claim Rejections - 35 USC § 103
The following is a quotation of 35 U.S.C. 103 which forms the basis for all obviousness rejections set forth in this Office action:
A patent for a claimed invention may not be obtained, notwithstanding that the claimed invention is not identically disclosed as set forth in section 102, if the differences between the claimed invention and the prior art are such that the claimed invention as a whole would have been obvious before the effective filing date of the claimed invention to a person having ordinary skill in the art to which the claimed invention pertains. Patentability shall not be negated by the manner in which the invention was made.
Claim(s) 1, 3 – 10, 12, 14 – 19 are rejected under 35 U.S.C. 103 as being unpatentable over Multi-fingered Robotic Hand Planner for Object Reconfiguration through a Rolling Contact Evolution Model Juan Antonio Corrales Ramón, et al. IEEE, 2013 (hereinafter referred to as Ramon) in view of Sun (US 9,321,176).
Claim 1, Ramon discloses a computer-implemented method for providing in hand robotics dexterous manipulation of an object, comprising: a position of the object, and a placement of at least one robotic finger of a robot upon the object, each robotic finger having a fingertip and multiple foldable joints; computing a direction of rolling or rotation of the object along a surface of the fingertip based on a contact of a fingertip of the at least one robotic finger [See Ramon, Intro I, page 625 – 626 - This paper presents a novel dexterous manipulation planner which uses a general representation of the surfaces of the fingers and the object so that it can be applied to any type of convex shape. Both the surfaces of the object and the fingers are represented as triangle meshes; See Table I, page 626, teaching obtain contact points on the fingertips and the object, obtain coordinates of the object, apply rolling and update the contact points on the object];
performing a matrix computation of the at least one robotic finger at a current joint configuration of the multiple foldable joints to transform a motion of the object along the fingertip surface in the direction of the rolling or rotation into joint motions of the multiple foldable joints; [see Ramon, Table 1, pages 626 – 629, Section B. Generation of Possible New Contact Configurations, transformation matrix and Section B. Contact Transitions Generated by Rolling, Fig. 5, teaching transformation matrices determining a change from the initial contact state towards a new final contact state; t, the planner implements a new contact evolution model based on a graph whose nodes represent contact states (i.e. pair of surface primitives that are in contact) and whose edges represent contact transitions generated by rolling movements between the primitives of these contact states];
updating a position of the object that is manipulated by the robot during the rolling or rotation along the fingertip surface, wherein the object is rolled or rotated along the fingertip without disengaging the contact between the fingertip and the object based on folding and unfolding motions of the multiple foldable joints of the at least one robotic finger [see Table 1, page 626, obtaining contacts between the fingers and the object, see 16. Update coordinates by applying rolling for each contact and calculate the position of each finger coordinate contact point. Further, each step of the hand trajectory computed by the planner is sent to the robot (simulation or real hand). The controller of the hand implements a hybrid position-force controller which guarantees not only that the hand reaches the commanded joint angles but also that their fingertips apply a minimum contact force which avoids undesired slippage; also, see Intro Page 625, the contact maintenance constraints guarantee that the fingers which touch the object do not break contact and thus the stability of the object is kept during the manipulation];
analyzing the updated contact points with respect to the geometry and position of the object during the rolling or rotation, and updating contact points of the fingertip of the at least one robotic finger with respect to the object during rolling or rotation of the object based on the joint motions of the multiple foldable joints of the at least one robotic finger in a manner that ensures that a viable grasp is enforced to have force closure to retain the object [see Ramon, Section II, page 626 - The proposed planner uses the geometric model of the surfaces of the object and the fingers in order to determine the evolution of the contacts between the fingers of the robotic hand and the manipulated object. The planner receives as input an initial grasp of the object and a trajectory of the object in task space. Further teaching manipulation of object is rolling, see Table 1, Fig 5, Section III, B. Contact transitions generated by rolling; Also, see Intro Page 625, the contact maintenance constraints guarantee that the fingers which touch the object do not break contact and thus the stability of the object is kept during the manipulation].
Ramon does not specifically teach determining a geometry of an object when the robotic fingertips are manipulated over the surface of the object.
However, Sun discloses enabling a user to demonstrate a grasp such that the type of grasp as well as the contact position and orientation of the thumb on the object can be learned from demonstration. The grasp type provides important task-oriented experience in terms of the way that a human grasps an object according to specific task purposes and the thumb position offers a general reference of the body part to be gripped [see Col.3, ll. 39 – 50].
Further teaching, eight daily objects, including vegetables and manipulation tools that comprise basic shapes such as cuboids, spheres, and cones, were tested in the experiments. These basic shapes form the majority of daily objects and manipulation tools. FIG. 11 illustrates the planning results of the eight objects. The ashtray, torus, and hammer were gripped with a power grasp; the bulb and cup were gripped with a precision sphere grasp; the onion was gripped with power sphere grasp; the floppy disk was gripped with a precision grasp; and the key was gripped with a lateral pinch grasp. Without a learning procedure, the optimization would result in a power grasp or a different contact part on the object body. For example, without integrating human strategy, the optimization of the grasp on the hammer would plan a power grasp on the head of the hammer rather than on the handle, which is less desirable in hammer manipulation tasks. Notice that when grasping an ashtray, because a human hand is much smaller than a Barrett hand, although the thumb contact point on the object was the same, the other fingers' contact points changed, resulting in different enclosures on the ashtray [see Col 10, line 39 – Col. 11, line 44 teaching experiments in simulation were conducted to examine how robust the resulting grasps of the proposed method are to resist small perception errors on object geometry and the relative thumb position to the target object. Five objects, including diverse shapes such as a sphere, a box, a cylinder, a torus, and a cone, were tested in the experiment. Only the precision grasp was tested because perception error is of higher concern for precision grasp than power grasp; therefore the fingertips on the robotic hand can determine the object shape and size, and best way to hold for manipulation].
Also teaching an object can be grasped in different ways or gripped at different locations, depending on specific purposes; therefore the controller must recognize determining a geometry of an object when the robotic fingertips are manipulated over the surface of the object; an example of different grasping methods of the same object is the procedure for screwing on a jar lid. This starts with a precision grasp using only the fingertips because the jar lid is loose at the beginning and can be screwed efficiently and flexibly without much power. As the jar lid gets tighter, one may switch to a power grasp to apply larger force on the jar lid [see Col.4, ll 63 – Col 5, line 8].
It would have been obvious before the effective date of the claimed invention to one of ordinary skill in the art to modify the device in Ramon, to include determining a geometry of an object when the robotic fingertips are manipulated over the surface of the object,
as suggested and taught by Sun, with a reasonable expectation of success, for the purpose of providing finding a grasp of good quality for a robotic hand to execute given an object and a manipulation task. A good quality grasp results from an appropriate placement of contacts on an object, also to ensure a stable grasp but not so strong as to damage the object, yet not weak enough to drop the object. The best known geometry of an object will allow precision grasping.
Claim 10 is similarly rejected as Claim 1, see above.
Claim 19, Ramon discloses a non-transitory computer readable storage medium storing instructions that when executed by a computer, which includes a processor performs a method, the method comprising: determining a geometry of an object, a position of the object, and a placement of at least one robotic finger of a robot upon the object, each robotic finger having a fingertip and multiple foldable joints computing a direction of rolling or rotation of the object along a surface of the fingertip by a contact of a fingertip of the at least one robotic finger; [See Ramon, Intro I, page 625 – 626 - This paper presents a novel dexterous manipulation planner which uses a general representation of the surfaces of the fingers and the object so that it can be applied to any type of convex shape. Both the surfaces of the object and the fingers are represented as triangle meshes; See Table I, page 626, teaching obtain contact points on the fingertips and the object, obtain coordinates of the object, apply rolling and update the contact points on the object];
performing a matrix computation of the at least one robotic finger at a current joint configuration of the multiple foldable joints to transform a motion of the object along the fingertip surface in the direction of the rolling or rotation into joint motions of the multiple foldable joints; [see Ramon, Table 1, pages 626 – 629, Section B. Generation of Possible New Contact Configurations, transformation matrix and Section B. Contact Transitions Generated by Rolling, Fig. 5, teaching transformation matrices determining a change from the initial contact state towards a new final contact state; t, the planner implements a new contact evolution model based on a graph whose nodes represent contact states (i.e. pair of surface primitives that are in contact) and whose edges represent contact transitions generated by rolling movements between the primitives of these contact states];
updating a position of the object that is manipulated by the robot during the rolling or rotation along the fingertip surface, wherein the object is rolled or rotated along the fingertip without disengaging the contact between the fingertip and the object based on folding and unfolding motions of the multiple foldable joints of the at least one robotic finger; [see Table 1, page 626, obtaining contacts between the fingers and the object, see 16. Update coordinates by applying rolling for each contact and calculate the position of each finger coordinate contact point. Further, each step of the hand trajectory computed by the planner is sent to the robot (simulation or real hand). The controller of the hand implements a hybrid position-force controller which guarantees not only that the hand reaches the commanded joint angles but also that their fingertips apply a minimum contact force which avoids undesired slippage; also, see Intro Page 625, the contact maintenance constraints guarantee that the fingers which touch the object do not break contact and thus the stability of the object is kept during the manipulation];
analyzing the updated contact points with respect to the geometry and position of the object during the rolling or rotation; and updating contact points of the fingertip of the at least one robotic finger with respect to the object during the rolling or rotation of the object based on the folding and unfolding motions of the multiple foldable joints of the at least one robotic finger in a manner that ensures that a viable grasp is enforced to have force closure to retain the object [see Ramon, Section II, page 626 - The proposed planner uses the geometric model of the surfaces of the object and the fingers in order to determine the evolution of the contacts between the fingers of the robotic hand and the manipulated object. The planner receives as input an initial grasp of the object and a trajectory of the object in task space. Further teaching manipulation of object is rolling, see Table 1, Fig 5, Section III, B. Contact transitions generated by rolling; Also, see Intro Page 625, the contact maintenance constraints guarantee that the fingers which touch the object do not break contact and thus the stability of the object is kept during the manipulation];
wherein computing the direction of rolling or rotation of the object along the fingertip surface by the contact of the fingertip includes determining the direction of the rolling or rotation by a computation of required folding and unfolding motions of the multiple foldable joints of the at least one robotic finger for the fingertip to roll or rotate the object in the direction along the fingertip surface, [See Ramon, Intro I, page 625 – 626 - This paper presents a novel dexterous manipulation planner which uses a general representation of the surfaces of the fingers and the object so that it can be applied to any type of convex shape. Both the surfaces of the object and the fingers are represented as triangle meshes; See Table I, page 626, teaching obtain contact points on the fingertips and the object, obtain coordinates of the object, apply rolling and update the contact points on the object; the planner has to compute the changes of the fingers joint angles which drive the object from the initial grasp towards the final desired configuration along the desired trajectory [see joint values in order to move the finger in C. Verification/Execution of feasible finger movements page 627];
wherein performing the matrix computation includes computing a Jacobian of the at least one robotic finger at a current joint configuration to transform a motion of the object in the direction of the rolling or rotation into joints motions [see Table I, pages 627 – 628 - the local planner also applies this rotation transformation to the fingertip position in order to calculate its new location (line 20 of Table I) and multiply it by the pseudo-inverse of the Jacobian matrix (line 21 of Table I), B. Generation of Possible New Contact Configurations and C. Verification/Execution of feasible finger movements; if the initial contact state is composed by the pair ( f O ,vFi ) , the following contact states could be generated by the rotation of the finger surface around the vertex vFi until an adjacent edge eFi (i.e. new contact state ( f O ,eFi ) ) or an adjacent face f Fi (i.e. new contact state ( f O , f Fi ) ) touches the object face f O].
Ramon does not specifically teach determining a geometry of an object when the robotic fingertips are manipulated over the surface of the object.
However, Sun discloses enabling a user to demonstrate a grasp such that the type of grasp as well as the contact position and orientation of the thumb on the object can be learned from demonstration. The grasp type provides important task-oriented experience in terms of the way that a human grasps an object according to specific task purposes and the thumb position offers a general reference of the body part to be gripped [see Col.3, ll. 39 – 50].
Further teaching, eight daily objects, including vegetables and manipulation tools that comprise basic shapes such as cuboids, spheres, and cones, were tested in the experiments. These basic shapes form the majority of daily objects and manipulation tools. FIG. 11 illustrates the planning results of the eight objects. The ashtray, torus, and hammer were gripped with a power grasp; the bulb and cup were gripped with a precision sphere grasp; the onion was gripped with power sphere grasp; the floppy disk was gripped with a precision grasp; and the key was gripped with a lateral pinch grasp. Without a learning procedure, the optimization would result in a power grasp or a different contact part on the object body. For example, without integrating human strategy, the optimization of the grasp on the hammer would plan a power grasp on the head of the hammer rather than on the handle, which is less desirable in hammer manipulation tasks. Notice that when grasping an ashtray, because a human hand is much smaller than a Barrett hand, although the thumb contact point on the object was the same, the other fingers' contact points changed, resulting in different enclosures on the ashtray [see Col 10, line 39 – Col. 11, line 44 teaching experiments in simulation were conducted to examine how robust the resulting grasps of the proposed method are to resist small perception errors on object geometry and the relative thumb position to the target object. Five objects, including diverse shapes such as a sphere, a box, a cylinder, a torus, and a cone, were tested in the experiment. Only the precision grasp was tested because perception error is of higher concern for precision grasp than power grasp; therefore the fingertips on the robotic hand can determine the object shape and size, and best way to hold for manipulation].
Also teaching an object can be grasped in different ways or gripped at different locations, depending on specific purposes; therefore the controller must recognize determining a geometry of an object when the robotic fingertips are manipulated over the surface of the object; an example of different grasping methods of the same object is the procedure for screwing on a jar lid. This starts with a precision grasp using only the fingertips because the jar lid is loose at the beginning and can be screwed efficiently and flexibly without much power. As the jar lid gets tighter, one may switch to a power grasp to apply larger force on the jar lid [see Col.4, ll 63 – Col 5, line 8].
It would have been obvious before the effective date of the claimed invention to one of ordinary skill in the art to modify the device in Ramon, to include determining a geometry of an object when the robotic fingertips are manipulated over the surface of the object, as suggested and taught by Sun, with a reasonable expectation of success, for the purpose of providing finding a grasp of good quality for a robotic hand to execute given an object and a manipulation task. A good quality grasp results from an appropriate placement of contacts on an object, also to ensure a stable grasp but not so strong as to damage the object, yet not weak enough to drop the object. The best known geometry of an object will allow precision grasping.
Claim 3, Ramon discloses the computer-implemented method of claim 1, wherein determining the placement of at least one robotic finger of a robot upon the object includes receiving tactile data associated with tactile sensors that are disposed upon the at least one robotic finger and determining contact points that pertain to a specific placement of the at least one robotic finger upon the object [see Page 629, Section IV - The dexterous manipulation planner described in the previous sections has been implemented as a C++ program which can communicate with a robotic simulator of the hand or with the real hand: a five-fingered Shadow Hand with 20 degrees-of-freedom (which mimics the kinematic structure of the human hand) and with tactile sensors over its fingertips (which are able to obtain the coordinates of the contact points and the corresponding contact 6D forces/torques).
Claim 12 is similarly rejected as Claim 3, see above.
Claim 4, Ramon discloses the computer-implemented method of claim 1, wherein computing a direction of rolling or rotation of the object along the fingertip surface includes determining the direction of the rolling or rotation by a computation of the required folding and unfolding motions of the multiple foldable joints of the at least one robotic finger for the fingertip to roll or rotate the object in the direction along the fingertip surface. [See Ramon, Intro I, page 625 – 626 - This paper presents a novel dexterous manipulation planner which uses a general representation of the surfaces of the fingers and the object so that it can be applied to any type of convex shape. Both the surfaces of the object and the fingers are represented as triangle meshes; See Table I, page 626, teaching obtain contact points on the fingertips and the object, obtain coordinates of the object, apply rolling and update the contact points on the object; the planner has to compute the changes of the fingers joint angles which drive the object from the initial grasp towards the final desired configuration along the desired trajectory [see joint values in order to move the finger in C. Verification/Execution of feasible finger movements page 627].
Claim 15 is similarly rejected as Claim 4, see above.
Claim 5, Ramon discloses the computer-implemented method of claim 4, wherein performing the matrix computation includes computing a Jacobian of the at least one robotic finger at the current joint configuration of the multiple foldable joints to transform the motion of the object along the fingertip surface into the joint motions [see Table I, pages 627 – 628 - the local planner also applies this rotation transformation to the fingertip position in order to calculate its new location (line 20 of Table I) and multiply it by the pseudo-inverse of the Jacobian matrix (line 21 of Table I), B. Generation of Possible New Contact Configurations and C. Verification/Execution of feasible finger movements; if the initial contact state is composed by the pair ( f O ,vFi ) , the following contact states could be generated by the rotation of the finger surface around the vertex vFi until an adjacent edge eFi (i.e. new contact state ( f O ,eFi ) ) or an adjacent face f Fi (i.e. new contact state ( f O , f Fi ) ) touches the object face f O].
Claim 14 is similarly rejected to Claim 5, see above.
Claim 6, Ramon discloses the computer-implemented method of claim 5, but is silent to further including determining at least one contact point of the at least one robotic finger that is to be updated to further roll or rotate the object in the direction during the rolling or rotation along the fingertip surface, wherein vectors of the normal and tangential forces applied by the at least one robotic finger are inside a friction cone of the contact point of the at least one robotic finger during the rolling or rotation.
However, Sun discloses a robot learns from observing humans grasp objects is called learning from demonstration (LfD). LfD has been a powerful mechanism for a teaching robot new tasks by observing people's demonstrations without any reprogramming. With the learning results, a robot can mimic human motions by reproducing movements similar to the demonstration. The LfD technique avoids a complex mathematic model for hands and objects, and provides useful task information from the demonstrations. Further teaching, a typical method for evaluating grasp quality is to compute force-closure, i.e., the ability of a grasp to resist external forces and moments in any direction. A force-closure property is quantified by the magnitude of the contact wrenches that can compensate for the disturbance wrench in the worst case. The wrench space is decided by contact points. Assuming a hard-contact model of the grasp, i.e., point contact with friction, a grasp consists of n contact points with friction. Let μ be the coefficient of friction between the hand and the object at any contact point. The most common friction model is Coulomb's friction model, which states that slippage is avoided when f.sup.t≦f.sup.n, where f.sup.t is the tangential force and f.sup.n is the normal force. According to the model, contact forces are constrained to lie within a cone whose vertex is at the contact point [see Col. 3, line 61 – Col, 4 line 14].
It would have been obvious before the effective date of the claimed invention to one of ordinary skill in the art to modify the device in Ramon, to include further including determining at least one contact point of the at least one robotic finger that is to be updated to further roll or rotate the object in the direction during the rolling or rotation along the fingertip surface, wherein vectors of the normal and tangential forces applied by the at least one robotic finger are inside a friction cone of the contact point of the at least one robotic finger during the rolling or rotation, as suggested and taught by Sun, with a reasonable expectation of success, for the purpose of providing finding a grasp of good quality for a robotic hand to execute given an object and a manipulation task. A good quality grasp results from an appropriate placement of contacts on an object, also to ensure a stable grasp but not so strong as to damage the object, yet not weak enough to drop the object.
Claim 15 is similarly rejected to Claim 6, see above.
Claim 7, Ramon discloses a computer-implemented method of claim 6, wherein at least one command is communicated to the robot to further roll or rotate the object in the direction and to update the position of the object [see Ramon, Table 1, pages 626 – 629, Section B. Generation of Possible New Contact Configurations, transformation matrix and Section B. Contact Transitions Generated by Rolling, Fig. 5, teaching transformation matrices determining a change from the initial contact state towards a new final contact state; t, the planner implements a new contact evolution model based on a graph whose nodes represent contact states (i.e. pair of surface primitives that are in contact) and whose edges represent contact transitions generated by rolling movements between the primitives of these contact states].
Claim 16 is similarly rejected as Claim 7, see above.
Claim 8, Ramon discloses the computer-implemented method of claim 7, further including analyzing the updated contact points with respect to the geometry and position of the object to determine if a current grasp has sufficient force closure to keep holding the object as it is manipulated during the rolling or rotation [See Ramon, Intro I, page 625 – 626 - This paper presents a novel dexterous manipulation planner which uses a general representation of the surfaces of the fingers and the object so that it can be applied to any type of convex shape. Both the surfaces of the object and the fingers are represented as triangle meshes; See Table I, page 626, teaching obtain contact points on the fingertips and the object, obtain coordinates of the object, apply rolling and update the contact points on the object].
Claim 17 is similarly rejected as Claim 8, see above.
Claim 9, Ramon discloses the computer-implemented method of claim 8, wherein at least one command is communicated to the robot to electronically control the at least one robotic finger to complete the required folding and unfolding motions of the multiple foldable joints based on computed required folding and unfolding motions of multiple foldable joints of the at least one robotic finger to maintain force closure of the object [see page 629, Fig. 7a, Section IV – the implemented program receives as input the desired object trajectory. Each step of the hand trajectory computed by the planner is sent to the robot (simulation or real hand). The controller of the hand implements a hybrid position-force controller which guarantees not only that the hand reaches the commanded joint angles but also that their fingertips apply a minimum contact force which avoids undesired slippage; Fig. 7a. shows the angles which have been executed by the joints of the fingers (TH -thumb- and FF -first finger-) during the translation of the cube. The hybrid position-force controller which executes the joint commands of the planner guarantees a minimum contact force of -0.8N in order to avoid contact breaking].
Claim 18 is similarly rejected as Claim 9, see above.
Claim(s) 2 and 11 are rejected under 35 U.S.C. 103 as being unpatentable over Multi-Fingered Robotic Hand Planner for Object Reconfiguration through a Rolling Contact Evolution Model Juan Antonio Corrales Ramón, et al. IEEE, 2013 (hereinafter referred to as Ramon) in view of Sun (US 9,321,176) and Control strategy for an industrial process monitoring robot, Fabian Zimber, 2016 (hereinafter referred to as Zimber).
Claim 2, Ramon discloses the computer-implemented method of claim 1, but is silent to wherein determining the geometry of an object and the position of the object include receiving image data and LiDAR data associated with images and a 3D point cloud of the robot and the object, wherein the image data and the LiDAR data is aggregated and analyzed to determine the geometry of the object and the position of the object as the robot is grasping the object.
However, Zimber discloses a controllable sensor system for enhanced process monitoring. The system is decoupled from the process tool and can therefore be controlled independently. It provides the machine operator with adaptive process information and
enhances the capabilities to qualify complex processes. We apply the concept to an autonomous sensor robot monitoring an industrial robot system for heavy, multipass TIG welding of voluminous workpieces.
Most importantly, Zimber teaches the use of LIDAR to track and confirm data that the robot uses while in use. The usage of a LiDAR sensor in the proposed system simplifies the tracking problem as the tracking is only relevant in a two-dimensional space (see Fig. 5).
Zimber additionally teaches that use of a camera system for robots, end effector – pick and place operations is well known in the art [See pages 706 II. – 708].
It would have been obvious before the effective date of the claimed invention to one of ordinary skill in the art to modify the device in Ramon to include wherein determining the geometry of an object and the position of the object include receiving image data and LIDAR data associated with images and a 3D point cloud of the robot and the object, wherein the image data and the LIDAR data is aggregated and analyzed to determine the geometry of the object and the position of the object as the robot is grasping the object, as suggested and taught by Zimber, with a reasonable expectation of success, for the purpose of providing improvements in the monitoring system which offers high flexibility for an operator and use of the lidar, cameras allows the operators to change their perspective to receive information about the process thus ensuring safety.
Claim 11 is similarly rejected as Claim 2, See above.
Response to Arguments
Applicant’s arguments with respect to all claim(s) have been considered but are moot because the new ground of rejection does not rely on any reference applied in the prior rejection of record for any teaching or matter specifically challenged in the argument.
Conclusion
Applicant's amendment necessitated the new ground(s) of rejection presented in this Office action. Accordingly, THIS ACTION IS MADE FINAL. See MPEP § 706.07(a). Applicant is reminded of the extension of time policy as set forth in 37 CFR 1.136(a).
A shortened statutory period for reply to this final action is set to expire THREE MONTHS from the mailing date of this action. In the event a first reply is filed within TWO MONTHS of the mailing date of this final action and the advisory action is not mailed until after the end of the THREE-MONTH shortened statutory period, then the shortened statutory period will expire on the date the advisory action is mailed, and any nonprovisional extension fee (37 CFR 1.17(a)) pursuant to 37 CFR 1.136(a) will be calculated from the mailing date of the advisory action. In no event, however, will the statutory period for reply expire later than SIX MONTHS from the mailing date of this final action.
The examiner has pointed out particular references contained in the prior art of record in the body of this action for the convenience of the applicant. Although the specified citations are representative of the teachings in the art and are applied to the specific limitations within the individual claim, other passages and figures may apply as well. Applicant should consider the entire prior art as applicable as to the limitations of the claims. It is respectfully requested from the applicant, in preparing the response, to consider fully the entire references as potentially teaching all or part of the claimed invention, as well as the context of the passage as taught by the prior art or disclosed by the examiner.
Any inquiry concerning this communication or earlier communications from the examiner should be directed to RENEE LAROSE whose telephone number is (313)446-4856. The examiner can normally be reached on Monday - Friday 8:30am - 5:00pm EST.
If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, Abby Lin can be reached on (571) 270-3976. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
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/Renee LaRose/Examiner, Art Unit 3657 /ABBY LIN/Supervisory Patent Examiner, Art Unit 3657