DETAILED ACTION
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 .
Response to Amendment
Examiner acknowledges receipt of Applicant’s amendments and arguments filed 03/13/2026. The arguments set forth are addressed herein below.
Applicant’s amendments necessitated the new ground of rejection set forth herein; therefore, this action is made Final.
The objection of the specification and the objection to the title are withdrawn in view of Applicant’s amendments.
The rejection of the claims under 35 U.S.C. 112(b)/112(f) is withdrawn. Applicant’s amendment replacing the “acquisition unit”, “generation unit”, and “output unit” limitations with “one or more processors configured to” is sufficient to overcome the invocation of 112(f) and the associated indefiniteness rejection.
The rejection of Claim 20 under 35 U.S.C. 101 is withdrawn. Amended claim 20 recites a “non-transitory computer readable medium” which removes the claims from the realm of transitory signals per se.
The rejection under 35 U.S.C. 103 is maintained as set forth below, modified to address the amended claim language and Applicant’s arguments.
Claims 1, 3-16, and 19-20 are now pending.
AIA Notice
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.
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 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.
The factual inquiries set forth in Graham v. John Deere Co., 383 U.S. 1, 148 USPQ 459 (1966), that are applied for establishing a background for determining obviousness under 35 U.S.C. 103 are summarized as follows:
1. Determining the scope and contents of the prior art.
2. Ascertaining the differences between the prior art and the claims at issue.
3. Resolving the level of ordinary skill in the pertinent art.
4. Considering objective evidence present in the application indicating obviousness or nonobviousness.
Claims 1, 3-16, and 18-20 are rejected under 35 U.S.C. 103 as being unpatentable over U.S. Patent Application Publication 2020/0122028 A1 to Konishi et al. (hereinafter Konishi) in view of U.S. Patent Application Publication 2021/0389825 A1 to Hamilton et al. (hereinafter Hamilton) and further in view of U.S. Patent Application Publication 2015/0339898 A1 to Saboune et al. (hereinafter Saboune).
Regarding Claim 1, and similarly recited Claims 16 and 20, (Currently Amended) Konishi discloses a signal generation device comprising: one or more processors (fig. 1, control section 11, ¶¶ [0030]-[0032] discloses control section 11 includes a program control device such as a central processing unit (CPU)) configured to:
acquire external parameters including parameters indicative of sensory characteristics (¶¶ [0041], [0074] discloses vibration data acquisition section 32 acquires the pseudo force sensory vibration data and the tactile sensory vibration data, which are both outputted from the application execution section 31 ….the tactile sensory vibration data and the pseudo force sensory vibration data each include encoded data indicative of an actual vibration waveform); and
… generating, based on the internal parameters, the waveform signal that presents a tactile sense matching a target psychological impression of a material texture (paras. [0037]-[0041] discloses the application execution section 31 outputs vibration instruction data including an instruction (vibration instruction) for vibrating the vibration device 20. The vibration instruction data may include, for example, data that is obtained by encoding the waveform of the vibration to be generated by the vibration mechanism 21. In such an instance, the amplitude and the frequency of the waveform determine the actual operation mode of the vibration mechanism 21 ... The tactile sensory vibration data is descriptive of a vibration that causes the user to feel a sensation (tactile sensation) as if the user has touched a certain object (this vibration is hereinafter referred to as the tactile sensory vibration). In general, the vibration causing the user to feel a tactile sensation is different from the vibration causing the user to feel the pseudo force sensation and is often generated by combining waveforms having a plurality of frequencies).
However Konishi does not explicitly disclose by:
mapping, using a machine learning model, the external parameters into internal parameters indicative of physical properties of vibration, wherein the machine learning model is trained to correlate external parameters with psychological impressions of material textures, wherein the external parameters include parameters related to virtual particles in a tactile sense presented by a vibration of the object to a user.
Hamilton discloses the use of machine learning to determine tactile point sets for a virtual object to model virtual objects, to select tactile effects (¶¶ [0027]-[0028] discloses using a set of machine learning models to quickly and efficiently determine a set of tactile points associated with a new virtual object. In specific examples, for instance, a set of learned machine learning models (e.g., deep learning models, neural networks, etc.) can be used to automatically assign a set of tactile points (e.g., based on a mesh) to a virtual object) and “… wherein the external parameters include parameters related to virtual particles in a tactile sense presented by a vibration of the object to a user” (…figs. 8, 10B, ¶¶ [0070], [0073], [0088]-[0094] discloses he tactile effects can also include one or more textures applied to a virtual object, which can cause perceptions of digital objects like spiky, bumpy, hairy, furry, soft, rough, and/or any other feelings … subset of inputs can optionally include a set of tactile points (equivalently referred to herein as a tactile point set), which can represent a virtual object, a tactile effect, any other tactile stimulation, and/or any combination)(tactile points equate to discrete focus points of stimulation defining a surface/texture).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to incorporate Hamilton’s tactile-point/texture representation with Konishi’s vibration apparatus because Hamilton teaches that representing tactile content as discrete points and texture parameters is an efficient way to define and manage how a surface or texture of a virtual object feels (Hamilton, ¶¶ [0028], [0073], [0088]-[0094]) and Konishi expressly seeks to convey the “feel, or texture” of a virtual object (Konishi, ¶¶ [0002]). The combination is the application of a known technique to a known device (Konishi’s controller) yielding the predictable result of rendering of texture parameters.
The combination of Konishi and Hamilton, does not explicitly disclose: “mapping, using a machine learning model, the external parameters into internal parameters indicative of physical properties of vibration, wherein the machine learning model is trained to correlate external parameters with psychological impressions of material textures.”
In a related invention, Saboune discloses a haptic authoring tool is provided that recommends one or more haptic effects provides a hipification model generated by learning a human haptic designer style. The haptic authoring tool receives an input comprising at least one of audio and video and a plurality of events. Saboune discloses mapping, using a machine learning model, the external parameters into internal parameters indicative of physical properties of vibration, wherein the machine learning model is trained to correlate external parameters with psychological impressions of material textures (¶¶ [0014], [0021]).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to incorporate Saboune’s machine-learning haptic tool with the combined system of Konishi/Hamilton as Saboune identifies the problem to tedious manual haptic design and solves it by learning a model that maps high-level descriptors to concrete effect parameters and outputs the effect matching the desired perception (Saboune, ¶¶ [0003]).
Regarding Claim 3, (Previously Presented) Konishi in view of Hamilton and Saboune discloses the signal generation device according to claim 1, wherein the external parameters include a parameter that specifies a degree of size of the virtual particles in the tactile sense (Hamilton, ¶¶ [0072]-[0073] discloses the adjustable parameters include a radius, number of tactile points, and slice angle. Geometric shapes can additionally or alternatively include polygons, with adjustable parameters including any or all of: a number of sides, a center-to-corner distance (e.g., a maximum radius of a circle enclosing the polygon), a number of points per side, and/or any of the parameters described above).
Regarding Claim 4. (Previously Presented) The signal generation device according to claim 1, wherein the external parameters includes a parameter that specifies a shape of the virtual particles in the tactile sense (Hamilton, ¶¶ [0072]-[0073] discloses the tactile effects can further include a representation of geometric shapes, such as a circle and/or ring (e.g., with a fixed radius)).
Regarding Claim 5, (Previously Presented) Konishi in view of Hamilton and Saboune discloses the signal generation device according to claim 1, wherein the external parameters include a parameter indicative of a degree of variation in at least one of a size and a shape of the virtual particles in the tactile sense.
Regarding Claim 6, (Previously Presented) Konishi in view of Hamilton and Saboune discloses the signal generation device according to claim 1, wherein the external parameters include parameters indicative of physical properties of the vibration (Konishi, ¶¶ [0074).
Regarding Claim 7 and similarly recited Claim 18, (Currently Amended) Konishi in view of Hamilton and Saboune discloses the signal generation device according to claim 1, wherein the one or more processors are further
Regarding Claim 8, (Currently Amended) Konishi in view of Hamilton and Saboune discloses the signal generation device according to claim 7, wherein the one or more processors are
Regarding Claim 9, (Original) Konishi in view of Hamilton and Saboune discloses the signal generation device according to claim 7, wherein the output includes an output of the internal parameters using a learned model obtained by learning a relationship between the external parameters and the internal parameters (Hamilton, ¶¶ [0027], [0109]; Saboune, ¶¶ [0012]).
Regarding Claim 10, (Original) Konishi in view of Hamilton and Saboune discloses the signal generation device according to claim 7, wherein the output includes an output of the internal parameters using a relationship between the external parameters and the internal parameters obtained by a statistical method (Saboune, figs. 4-5, ¶¶ [0033]-[0034], [0043]-[0046]).
Regarding Claim 11, (Original) Konishi in view of Hamilton and Saboune discloses the signal generation device according to claim 7, wherein the output includes an output of the internal parameters using an affine transformation prescribed to calculate the internal parameters from the external parameters (Hamilton, ¶¶ [0069], [0148]).
Regarding Claim 12, (Currently Amended) Konishi in view of Hamilton and Saboune discloses the signal generation device according to claim 7, wherein the one or more processors are one or more processors
Regarding Claim 13, (Original) Konishi in view of Hamilton and Saboune discloses the signal generation device according to claim 1, wherein the object is a game controller (Konishi, ¶¶ [0029]-[0030]).
Regarding Claim 14 and similarly recited Claim 19, (Original) Konishi in view of Hamilton and Saboune discloses the signal generation device according to claim 1, wherein the waveform signal is configured as an electronic signal that is converted by a haptic element in the object to cause the object to vibrate as a mechanical vibration (Konishi, ¶¶ [0029], [0043]-[0044] discloses vibration mechanism 21).
Regarding Claim 15, (Original) Konishi in view of Hamilton and Saboune discloses the signal generation device according to claim 14, wherein the haptic element is at least one of an eccentric motor, a linear resonant actuator, an electromagnetic actuator, a piezoelectric actuator, an ultrasonic actuator, an electrostatic actuator, and a polymer actuator (Konishi, ¶¶ [0029] discloses the vibration mechanism 21 may be a linear resonance actuator, a voice coil motor, an eccentric motor, or another vibration generation element; see also Hamilton, ¶¶ [0036]).
Response to Arguments/Remarks
Applicant’s arguments filed 03/13/2026 have been fully considered.
Applicant argues, on pages 2-3, that neither Konishi and/or Hamilton teaches/discloses (1) "mapping, using a machine learning model, the external parameters into internal parameters indicative of physical properties of vibration, wherein the machine learning model is trained to correlate external parameters with psychological impressions of material textures," as recited in amended independent claims 1, 16, and 20 and (2) "generating, based on the internal parameters, the waveform signal that presents a tactile sense matching a target psychological impression of a material texture," as recited in amended independent claims 1, 16, and 20. These are newly recited amendments and are acknowledged by the Examiner. The current rejection addresses these newly recited amendments with a new rejection above.
Applicant’s remaining arguments directed to the rejections under 35 USC 112, 101, and objection grounds are moot in view of the withdrawals mentioned above.
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.
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/S.N.H/Examiner, Art Unit 3715
/XUAN M THAI/Supervisory Patent Examiner, Art Unit 3715
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