DETAILED ACTION
Claims 1-20 filed January 7th 2026 are pending in the current 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 Arguments
Applicant’s arguments with respect to claim(s) 1-20have 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.
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 (i.e., changing from AIA to pre-AIA ) for the rejection will not be considered a new ground of rejection if the prior art relied upon, and the rationale supporting the rejection, would be the same under either status.
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) 1-8, 10, 12-17, 19 is/are rejected under 35 U.S.C. 103 as being unpatentable over Domenikos et al. (US2019/0171291) in view of Cao (US2019/0156567) in view of Beattie et al. (US2020/0218354)
Consider claim 1, where Domenikos teaches a tactile rendering system, comprising: a texture image processing subsystem, including: an image capture module, used to obtain a real material surface image; (See Domenikos ¶30 where the touch enabling platform (TEP) allows for uploading from an image source (mobile phone, digital camera, storage device, etc…), the detection of a large number of different categories of everyday objects in uploaded photos and videos) and a texture image processing module, used to obtain a real texture image according to the real material surface image; (See Domenikos ¶30 where the touch enabling platform (TEP) allows for API calls to fetch the details of haptic properties for all objects embedded in a video or photograph in a tagged format) a generative texture image comparison subsystem, including: a texture image feature factor capturing module, used to analyze at least one real texture image feature factor according to the real texture image; (See Domenikos ¶26 where the touch enablement platform according to embodiments allows for automatic detection and identification of objects in videos and images. Each detected object can be endowed with corresponding haptic properties automatically. In some cases, the haptic experience may be subject to limitations of a best match with the surface contours and the closest available texture in the library.) a tactile feature database; and a tactile feature database factor search module, used to search the tactile feature database to obtain at least one tactile data; (See Domenikos ¶22, 26 where in some cases, the haptic experience may be subject to limitations of a best match with the surface contours and the closest available texture in the library.) and a tactile rendering subsystem, including: a tactile human rendering generation module, used to generate at least one tactile rendering signal according to the at least one tactile data. (See Domenikos ¶32-33, 53 where the core haptic engine (CHE) generates tactile signals that match the virtual object during tactile exploration)
Domenikos teaches retrieving the closest available texture in the library, however Domenikos does not explicitly teach according to the at least one real texture image feature factor. However, in an analogous field of endeavor Cao teaches according to the at least one real texture image feature factor. (See Cao ¶42 where the texture information extraction unit will extract a texture feature from the preprocessed image; to calculate a degree of matching between the extracted texture feature of the image and a texture feature of each template texture in a pre-established texture database) Therefore, it would have been obvious for one of ordinary skill in the art that the haptic properties extracted from the image of Domenikos would be used to calculate a degree of matching as taught by Cao in order to extract the best match as taught by Domenikos. It would have been obvious for one of ordinary skill in the art to use the existing information to perform the search in the existing library.
Domenikos teaches textures in a library, however Domenikos does not explicitly teach wherein the tactile data includes a quantification result of image pattern, and the quantification result of the image pattern includes a gray-scale co-occurrence matrix energy value, an entropy value, a contrast value, or a contrast difference value. However, in an analogous field of endeavor Beattie teaches wherein the tactile data includes a quantification result of image pattern, and the quantification result of the image pattern includes a gray-scale co-occurrence matrix energy value, an entropy value, a contrast value, or a contrast difference value. (See Beattie ¶66-70 where In association with gray-level co-occurrence matrices GLCMs are various second-order texture measures known as Haralick features. Haralick first proposed 14 types of feature statistics based on GLCMs. These can be split into 3 groups: Contrast, orderliness, and descriptives, as previous work has shown these groups to be independent of each other. Within the Contrast group, measures of contrast (CON), dissimilarity (DIS), and homogeneity (HOM)/inverse difference moment (IDM), exist to explain the relative depth and smoothness of an image texture at a given offset. Angular second moment (ASM), or energy (√ASM), and entropy (ENT), all contribute towards assessments of orderliness of pixel gray-level dispersion within an image texture.) Therefore, it would have been obvious for one of ordinary skill in the art that the textures being stored in the texture library of Domenikos would have been stored as Haralick features as taught by Beattie. One of ordinary skill in the art would have been motivated to perform the modification for the advantage of/ benefit of using known parameters for image texture analysis to yield the desired result of a texture library.
Consider claim 2, where Domenikos in view of Cao in view of Beattie teaches the tactile rendering system according to claim 1, wherein the texture image processing subsystem further includes: a tactile parameter input module, used to input at least one material surface property parameter, wherein the tactile feature database factor search module obtains at least one reference texture image feature factor. (See Domenikos ¶26 where the touch enablement platform according to embodiments allows for automatic detection and identification of objects in videos and images. Each detected object can be endowed with corresponding haptic properties automatically. In some cases, the haptic experience may be subject to limitations of a best match with the surface contours and the closest available texture in the library.)
Domenikos teaches obtaining a best match from a texture in the library, however Domenikos does not explicitly teach according to the at least one material surface property parameter and the at least one real texture image feature factor; the generative texture image comparison subsystem further includes: a new texture image generation module, used to generate a new texture image according to the at least one real texture image feature factor; a reference texture image generation module, used to generate a reference texture image according to the at least one reference texture image feature factor; and a texture image similarity comparison module, used to compare the new texture image and the reference texture image, wherein if a similarity between the new texture image and the reference texture image is greater than a predetermined value, the at least one tactile data corresponding to the at least one reference texture image feature factor is outputted. However, in an analogous field of endeavor Cao teaches according to the at least one material surface property parameter and the at least one real texture image feature factor; (See Cao ¶42 where the texture information extraction unit will extract a texture feature from the preprocessed image; to calculate a degree of matching between the extracted texture feature of the image and a texture feature of each template texture in a pre-established texture database) the generative texture image comparison subsystem further includes: a new texture image generation module, used to generate a new texture image according to the at least one real texture image feature factor; a reference texture image generation module, used to generate a reference texture image according to the at least one reference texture image feature factor; and a texture image similarity comparison module, used to compare the new texture image and the reference texture image, wherein if a similarity between the new texture image and the reference texture image is greater than a predetermined value, the at least one tactile data corresponding to the at least one reference texture image feature factor is outputted. (See Cao ¶42, 65 where a degree of matching is calculated and if the particular attributes do not conform to any of those predefined in the database a default model is chosen. Thus, the degree of matching of the selected texture is higher than the degree of matching to a default option.) Therefore, it would have been obvious for one of ordinary skill in the art that the haptic properties extracted from the image of Domenikos would be used to calculate a degree of matching as taught by Cao in order to extract the best match as taught by Domenikos. It would have been obvious for one of ordinary skill in the art to use the existing information to perform the search in the existing library.
Consider claim 3, where Domenikos in view of Cao in view of Beattie teaches the tactile rendering system according to claim 1, wherein the tactile rendering subsystem further includes: a tactile signal conversion module, used to convert the at least one tactile rendering signal to at least one actuation signal. (See Domenikos ¶104 where the actuation commands are sent to the actuator(s) in use)
Consider claim 4, where Domenikos in view of Cao in view of Beattie teaches the tactile rendering system according to claim 3, wherein a quantity of the at least one actuation signal is a plurality, and the actuation signals are used to control at least one tactile feedback module in a multiple access manner. (See Domenikos ¶104, 58-65 where the actuation commands are sent to the actuator(s) in use and the actuators may be implemented in a variety of hardware options as outlined by Domenikos ¶58-65)
Consider claim 5, where Domenikos in view of Cao in view of Beattie teaches the tactile rendering system according to claim 3, wherein a quantity of at least one actuation signal is a plurality, and the actuation signals are used to control at least one tactile feedback module in a spatial separation manner. (See Domenikos ¶67 where actuation can be targeted towards a specific area such as the fingertip or segments of the finger)
Consider claim 6, where Domenikos in view of Cao in view of Beattie teaches the tactile rendering system according to claim 1, wherein the tactile rendering subsystem further includes: a user posture tracking module, used to obtain a user posture; and a displacement information analysis module, used to analyze a displacement information according to the user posture; (See Domenikos ¶70 where one or more positional tracker(s) 160 can be used independently or as a complement of the on-board sensors of touch interface device(s) 140 to allow CHE system 126 to compute position and orientation of the user 112. In various embodiments, one or more positional tracker(s) 160 can be located within touch interface device(s) 140 to provide information about the position of one or more of these devices (and user 112), which may be transmitted/stored/manipulated as sensor data 60.) wherein tactile human rendering generation module generates the tactile rendering signal further according to the displacement information. (See Domenikos’ abstract and ¶70, 117 where a characteristic of the contact of the user and the touch interface is used to determine the waveform the actuator vibration wherein the characteristic may be the physical relationship (e.g., complementary, size-coincident, etc.) between the device and the other component can aid in performing a function, for example, displacement of one or more of the device or other components. )
Consider claim 7, where Domenikos in view of Cao in view of Beattie teaches the tactile rendering system according to claim 1, wherein the tactile rendering subsystem further includes: a user human factor input module, used to input a user human factor; wherein the tactile human rendering generation module optimizes the at least one tactile rendering signal according to the user human factor. (See Domenikos ¶70 where the I/O component 108 can comprise one or more human I/O devices, which enable user(s) (e.g., a human and/or computerized user) 112 to interact with the computer system 102 and/or one or more communications devices to enable the system user(s) 112 to communicate with the computer system 102 using any type of communications link. To this extent, the CHE system 126 can manage a set of interfaces (e.g., graphical user interface(s), application program interface) that enable human and/or system users(s) 112 to interact with the CHE system 126. Thus, optimizing for human interaction with a graphical user interface)
Consider claim 8, where Domenikos in view of Cao in view of Beattie teaches the tactile rendering system according to claim 1, wherein the texture image processing subsystem further includes: a virtual object selection module, used to obtain at least one virtual object, (See Domenikos ¶26 where the touch enablement platform according to embodiments allows for automatic detection and identification of objects in videos and images. Each detected object can be endowed with corresponding haptic properties automatically. In some cases, the haptic experience may be subject to limitations of a best match with the surface contours and the closest available texture in the library.) wherein the texture image processing module obtains the real texture image according to the real material surface image and the virtual object. (See Domenikos ¶70 where the sensors are for creating a tactile response to a virtual object or environment)
Consider claim 10, where Domenikos in view of Cao in view of Beattie teaches the tactile rendering system according to claim 1, wherein the texture image processing subsystem further includes: an inertial data capturing module, used to capture an inertia data, wherein the texture image feature factor capturing module analyzes the real texture image feature factor according to the real texture image and the inertia data. (See Domenikos ¶70 where touch interface device 140 can include (or be coupled to) one or more sensors (e.g., magnetic, inertial, optical) and actuators (e.g., vibrational, electromagnetic, piezoelectric) for creating a tactile response to a virtual object or environment.)
Consider claim 12, where Domenikos teaches a tactile rendering method, comprising: obtaining a real material surface image; (See Domenikos ¶30 where the touch enabling platform (TEP) allows for uploading from an image source (mobile phone, digital camera, storage device, etc…), the detection of a large number of different categories of everyday objects in uploaded photos and videos) obtaining a real texture image according to the real material surface image; (See Domenikos ¶30 where the touch enabling platform (TEP) allows for API calls to fetch the details of haptic properties for all objects embedded in a video or photograph in a tagged format) obtaining at least one real texture image feature factor according to the real texture image; searching a tactile feature database, to obtain at least one tactile data; and generating at least one tactile rendering signal according to the at least one tactile data. (See Domenikos ¶26 where the touch enablement platform according to embodiments allows for automatic detection and identification of objects in videos and images. Each detected object can be endowed with corresponding haptic properties automatically. In some cases, the haptic experience may be subject to limitations of a best match with the surface contours and the closest available texture in the library.)
Domenikos teaches retrieving the closest available texture in the library, however Domenikos does not explicitly teach according to the at least one real texture image feature factor. However, in an analogous field of endeavor Cao teaches according to the at least one real texture image feature factor. (See Cao ¶42 where the texture information extraction unit will extract a texture feature from the preprocessed image; to calculate a degree of matching between the extracted texture feature of the image and a texture feature of each template texture in a pre-established texture database) Therefore, it would have been obvious for one of ordinary skill in the art that the haptic properties extracted from the image of Domenikos would be used to calculate a degree of matching as taught by Cao in order to extract the best match as taught by Domenikos. It would have been obvious for one of ordinary skill in the art to use the existing information to perform the search in the existing library.
Domenikos teaches textures in a library, however Domenikos does not explicitly teach wherein the tactile data includes a quantification result of image pattern, and the quantification result of the image pattern includes a gray-scale co-occurrence matrix energy value, an entropy value, a contrast value, or a contrast difference value. However, in an analogous field of endeavor Beattie teaches wherein the tactile data includes a quantification result of image pattern, and the quantification result of the image pattern includes a gray-scale co-occurrence matrix energy value, an entropy value, a contrast value, or a contrast difference value. (See Beattie ¶66-70 where In association with gray-level co-occurrence matrices GLCMs are various second-order texture measures known as Haralick features. Haralick first proposed 14 types of feature statistics based on GLCMs. These can be split into 3 groups: Contrast, orderliness, and descriptives, as previous work has shown these groups to be independent of each other. Within the Contrast group, measures of contrast (CON), dissimilarity (DIS), and homogeneity (HOM)/inverse difference moment (IDM), exist to explain the relative depth and smoothness of an image texture at a given offset. Angular second moment (ASM), or energy (√ASM), and entropy (ENT), all contribute towards assessments of orderliness of pixel gray-level dispersion within an image texture.) Therefore, it would have been obvious for one of ordinary skill in the art that the textures being stored in the texture library of Domenikos would have been stored as Haralick features as taught by Beattie. One of ordinary skill in the art would have been motivated to perform the modification for the advantage of/ benefit of using known parameters for image texture analysis to yield the desired result of a texture library.
Consider claim 13, where Domenikos in view of Cao in view of Beattie teaches the tactile rendering method according to claim 12, further comprising: generating a new texture image according to the at least one real texture image feature factor; inputting at least one material surface property parameter; obtaining at least one reference texture image feature factor according to the at least one material surface property parameter and. (See Domenikos ¶26 where the touch enablement platform according to embodiments allows for automatic detection and identification of objects in videos and images. Each detected object can be endowed with corresponding haptic properties automatically. In some cases, the haptic experience may be subject to limitations of a best match with the surface contours and the closest available texture in the library.)
Domenikos teaches obtaining a best match from a texture in the library, however Domenikos does not explicitly teach the at least one real texture image feature factor; generating a reference texture image according to the at least one reference texture image feature factor; and comparing the new texture image and the reference texture image, wherein if a similarity between the new texture image and the reference texture image is greater than a predetermined value, the at least one tactile data corresponding to the at least one reference texture image feature factor is outputted. However, in an analogous field of endeavor Cao teaches the at least one real texture image feature factor; generating a reference texture image according to the at least one reference texture image feature factor; (See Cao ¶42 where the texture information extraction unit will extract a texture feature from the preprocessed image; to calculate a degree of matching between the extracted texture feature of the image and a texture feature of each template texture in a pre-established texture database) and comparing the new texture image and the reference texture image, wherein if a similarity between the new texture image and the reference texture image is greater than a predetermined value, the at least one tactile data corresponding to the at least one reference texture image feature factor is outputted. (See Cao ¶42, 65 where a degree of matching is calculated and if the particular attributes do not conform to any of those predefined in the database a default model is chosen. Thus, the degree of matching of the selected texture is higher than the degree of matching to a default option.) Therefore, it would have been obvious for one of ordinary skill in the art that the haptic properties extracted from the image of Domenikos would be used to calculate a degree of matching as taught by Cao in order to extract the best match as taught by Domenikos. It would have been obvious for one of ordinary skill in the art to use the existing information to perform the search in the existing library.
Consider claim 14, where Domenikos in view of Cao in view of Beattie teaches the tactile rendering method according to claim 12, further comprising: converting the at least one tactile rendering signal into at least one actuation signal. (See Domenikos ¶104 where the actuation commands are sent to the actuator(s) in use)
Consider claim 15, where Domenikos in view of Cao in view of Beattie teaches the tactile rendering method according to claim 12, further comprising: obtaining a user posture; and obtaining a displacement information according to the user posture, (See Domenikos ¶70 where one or more positional tracker(s) 160 can be used independently or as a complement of the on-board sensors of touch interface device(s) 140 to allow CHE system 126 to compute position and orientation of the user 112. In various embodiments, one or more positional tracker(s) 160 can be located within touch interface device(s) 140 to provide information about the position of one or more of these devices (and user 112), which may be transmitted/stored/manipulated as sensor data 60.) and optimizing the tactile rendering signal according to the displacement information. (See Domenikos’ abstract and ¶70, 117 where a characteristic of the contact of the user and the touch interface is used to determine the waveform the actuator vibration wherein the characteristic may be the physical relationship (e.g., complementary, size-coincident, etc.) between the device and the other component can aid in performing a function, for example, displacement of one or more of the device or other components. )
Consider claim 16, where Domenikos in view of Cao in view of Beattie teaches the tactile rendering method according to claim 12, further comprising: optimizing the at least one tactile rendering signal according to a user human factor. (See Domenikos ¶70 where the I/O component 108 can comprise one or more human I/O devices, which enable user(s) (e.g., a human and/or computerized user) 112 to interact with the computer system 102 and/or one or more communications devices to enable the system user(s) 112 to communicate with the computer system 102 using any type of communications link. To this extent, the CHE system 126 can manage a set of interfaces (e.g., graphical user interface(s), application program interface) that enable human and/or system users(s) 112 to interact with the CHE system 126. Thus, optimizing for human interaction with a graphical user interface)
Consider claim 17, where Domenikos in view of Cao in view of Beattie teaches the tactile rendering method according to claim 12, further comprising: providing at least one virtual object, and optimizing the real texture image according to the at least one virtual object. (See Domenikos ¶70 where the sensors are for creating a tactile response to a virtual object or environment)
Consider claim 19, where Domenikos in view of Cao in view of Beattie teaches the tactile rendering method according to claim 12, further comprising: capturing an inertia data, wherein the at least one real texture image feature factor is obtained according to the real texture image and the inertia data. (See Domenikos ¶70 where touch interface device 140 can include (or be coupled to) one or more sensors (e.g., magnetic, inertial, optical) and actuators (e.g., vibrational, electromagnetic, piezoelectric) for creating a tactile response to a virtual object or environment.)
Claim(s) 9, 11, 18 and 20 is/are rejected under 35 U.S.C. 103 as being unpatentable over Domenikos in view of Cao in view of Beattie as applied to claim 1 above, in further view of Mullins et al. (US2016/0217590)
Consider claim 9, where Domenikos in view of Cao in view of Beattie teaches the tactile rendering system according to claim 1, however, Domenikos does not explicitly teach wherein the texture image processing subsystem further includes: a previous image comparison module, used to compare the real material surface image and a previous real material surface image; the tactile rendering subsystem further includes: a reference tactile signal module, wherein if a difference between the real material surface image and the previous real material surface image is lower than a predetermined value, the previous tactile signal module provides at least one reference tactile rendering signal; (See Cao ¶42 where the texture information extraction unit will extract a texture feature from the preprocessed image; to calculate a degree of matching between the extracted texture feature of the image and a texture feature of each template texture in a pre-established texture database) Therefore, it would have been obvious for one of ordinary skill in the art that the haptic properties extracted from the image of Domenikos would be used to calculate a degree of matching as taught by Cao in order to extract the best match as taught by Domenikos. It would have been obvious for one of ordinary skill in the art to use the existing information to perform the search in the existing library.
Cao teaches a texture database; however Cao does not explicitly teach a previous tactile signal; the tactile human rendering generation module adjusts the at least one previous tactile rendering signal according to a difference between the real material surface image and the tactile rendering signal to obtain the at least one tactile rendering signal. However, in an analogous field of endeavor Mullins teaches a previous tactile signal; the tactile human rendering generation module adjusts the at least one previous tactile rendering signal according to a difference between the real material surface image and the tactile rendering signal to obtain the at least one tactile rendering (See Mullins Figs 6, 7, and ¶73-84 where a real object is identified a first time at step 608 and subsequently identified again in step 706 the textures of the virtual object are adjusted based upon the texture mapping on the real object. Thus, lowest difference) Therefore, it would have been obvious that the reference database of Cao could be updated with previous inputs as taught by Mullins. One of ordinary skill in the art would have been motivated to perform the modification for the advantage of/ benefit of reducing the need to re-render objects to conserve computing power (See Mullins ¶84)
Consider claim 11, where Domenikos in view of Cao in view of Beattie teaches the tactile rendering system according to claim 1, wherein the texture image processing subsystem further includes: a reference image comparison module, used to compare the real material surface image and a previous real material surface image; the tactile rendering subsystem further includes: a previous tactile signal module, wherein if a similarity between the real material surface image and the previous real material surface image is higher than a predetermined value, the reference tactile signal module provides at least one reference tactile rendering signal; (See Cao ¶42 where the texture information extraction unit will extract a texture feature from the preprocessed image; to calculate a degree of matching between the extracted texture feature of the image and a texture feature of each template texture in a pre-established texture database. Thus, highest similarity.) Therefore, it would have been obvious for one of ordinary skill in the art that the haptic properties extracted from the image of Domenikos would be used to calculate a degree of matching as taught by Cao in order to extract the best match as taught by Domenikos. It would have been obvious for one of ordinary skill in the art to use the existing information to perform the search in the existing library.
Cao teaches a reference material; however Cao does not explicitly teach a previous tactile signal module and a tactile signal conversion module, used to convert the least one tactile rendering signal or the at least one previous tactile rendering signal to at least one actuation signal. However, in an analogous field of endeavor Mullins teaches a previous tactile signal module and a tactile signal conversion module, used to convert the least one tactile rendering signal or the at least one previous tactile rendering signal to at least one actuation signal. (See Mullins Figs 6, 7, and ¶73-84 where a real object is identified a first time at step 608 and subsequently identified again in step 706 the textures of the virtual object are adjusted based upon the texture mapping on the real object. Thus, lowest difference) Therefore, it would have been obvious that the reference database of Cao could be updated with previous inputs as taught by Mullins. One of ordinary skill in the art would have been motivated to perform the modification for the advantage of/ benefit of reducing the need to re-render objects to conserve computing power (See Mullins ¶84)
Consider claim 18, where Domenikos in view of Cao in view of Beattie teaches the tactile rendering method according to claim 12, further comprising: comparing the real material surface image with a reference real material surface image; (See Cao ¶42 where the texture information extraction unit will extract a texture feature from the preprocessed image; to calculate a degree of matching between the extracted texture feature of the image and a texture feature of each template texture in a pre-established texture database) Therefore, it would have been obvious for one of ordinary skill in the art that the haptic properties extracted from the image of Domenikos would be used to calculate a degree of matching as taught by Cao in order to extract the best match as taught by Domenikos. It would have been obvious for one of ordinary skill in the art to use the existing information to perform the search in the existing library.
Cao teaches a reference material, however Cao does not explicitly teach a previous tactile signal module and adjusting at least one previous tactile rendering signal to obtain the tactile rendering signal, if a difference between the real material surface image and the previous real material surface image is lower than a predetermined value. However, in an analogous field of endeavor Mullins teaches a previous tactile signal module and adjusting at least one previous tactile rendering signal to obtain the tactile rendering signal, if a difference between the real material surface image and the previous real material surface image is lower than a predetermined value. (See Mullins Figs 6, 7, and ¶73-84 where a real object is identified a first time at step 608 and subsequently identified again in step 706 the textures of the virtual object are adjusted based upon the texture mapping on the real object. Thus, lowest difference) Therefore, it would have been obvious that the reference database of Cao could be updated with previous inputs as taught by Mullins. One of ordinary skill in the art would have been motivated to perform the modification for the advantage of/ benefit of reducing the need to re-render objects to conserve computing power (See Mullins ¶84)
Consider claim 20, where Domenikos in view of Cao in view of Beattie teaches the tactile rendering method according to claim 12, further comprising: comparing the real material surface image with a reference real material surface image; (See Cao ¶42 where the texture information extraction unit will extract a texture feature from the preprocessed image; to calculate a degree of matching between the extracted texture feature of the image and a texture feature of each template texture in a pre-established texture database) Therefore, it would have been obvious for one of ordinary skill in the art that the haptic properties extracted from the image of Domenikos would be used to calculate a degree of matching as taught by Cao in order to extract the best match as taught by Domenikos. It would have been obvious for one of ordinary skill in the art to use the existing information to perform the search in the existing library.
Cao teaches a reference material; however Cao does not explicitly teach the previous real material surface image is higher than a predetermined value; and converting the at least one tactile rendering signal or the at least one previous tactile rendering signal into at least one actuation signal.
However, in an analogous field of endeavor Mullins teaches the previous real material surface image is higher than a predetermined value; and converting the at least one tactile rendering signal or the at least one previous tactile rendering signal into at least one actuation signal. (See Mullins Figs 6, 7, and ¶73-84 where a real object is identified a first time at step 608 and subsequently identified again in step 706 the textures of the virtual object are adjusted based upon the texture mapping on the real object. Thus, lowest difference) Therefore, it would have been obvious that the reference database of Cao could be updated with previous inputs as taught by Mullins. One of ordinary skill in the art would have been motivated to perform the modification for the advantage of/ benefit of reducing the need to re-render objects to conserve computing power (See Mullins ¶84)
Conclusion
THIS ACTION IS MADE FINAL. 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.
Any inquiry concerning this communication or earlier communications from the examiner should be directed to WILLIAM LU whose telephone number is (571)270-1809. The examiner can normally be reached 10am-6:30pm.
Examiner interviews are available via telephone, in-person, and video conferencing using a USPTO supplied web-based collaboration tool. To schedule an interview, applicant is encouraged to use the USPTO Automated Interview Request (AIR) at http://www.uspto.gov/interviewpractice.
If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, Matthew Eason can be reached at 571-270-7230. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
Information regarding the status of published or unpublished applications may be obtained from Patent Center. Unpublished application information in Patent Center is available to registered users. To file and manage patent submissions in Patent Center, visit: https://patentcenter.uspto.gov. Visit https://www.uspto.gov/patents/apply/patent-center for more information about Patent Center and https://www.uspto.gov/patents/docx for information about filing in DOCX format. For additional questions, contact the Electronic Business Center (EBC) at 866-217-9197 (toll-free). If you would like assistance from a USPTO Customer Service Representative, call 800-786-9199 (IN USA OR CANADA) or 571-272-1000.
WILLIAM LU
Primary Examiner
Art Unit 2624
/WILLIAM LU/Primary Examiner, Art Unit 2624