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 .
Status of Claims
This action is responsive to Applicant’s claims filed 08/23/2024.
Claims 1-26 are currently pending and have been examined here.
Claims 1-26 have been amended.
Claim Objections
Claims 3-13 and 23-24 are objected to as being dependent upon a rejected base claim, but would be allowable if rewritten in independent form including all of the limitations of the base claim and any intervening claims.
Claim Rejections - 35 USC § 101
35 U.S.C. 101 reads as follows:
Whoever invents or discovers any new and useful process, machine, manufacture, or composition of matter, or any new and useful improvement thereof, may obtain a patent therefor, subject to the conditions and requirements of this title.
Claim 25 is rejected under 35 U.S.C. 101. Claims are ineligible for patent protection if they are drawn to subject matter which is not within one of the four statutory categories, or, if the subject matter claimed does fall into one of the four statutory categories, the claims are ineligible if they recite a judicial exception, are directed to that judicial exception, and do not recite additional elements which amount to significantly more than the judicial exception itself. Alice Corp. v. CLS Bank Int'l, 375 U.S. ___ (2014). Accordingly, claims are first analyzed to determine whether they fall into one of the four statutory categories of patent eligible subject matter. Then, if the claims fall within one of the four statutory categories, it must be determined whether the claims are directed to a judicial exception to patentability (i.e., a law of nature, a natural phenomenon, or an abstract idea).
As per claim 25, the claim is rejected under 35 U.S.C. 101 because it is drawn to ineligible patent subject matter. The claims are not directed to one of the four statutory categories. As per claim 15 the claim is directed to “A computer program product, wherein the computer program product stores instructions, and when the instructions are executed by a processing device to perform the method of claim 20. The broadest reasonable interpretation of the term “computer program product” includes mere software per so. Claims which are directed to software per se are directed to patent ineligible subject matter. See In re Nuijten, 500 F.3d 1346, 84 USPQ2d 1495 (Fed. Cir. 2007). No other physical components are required by the claim. Thus, the claim is directed to subject matter which encompasses software per se within its broadest reasonable interpretation, and is thus not directed to patent eligible subject matter under 35 U.S.C. 101.
As per claim 25, the claim is rejected under 35 U.S.C. 101 because it is drawn to ineligible patent subject matter. The claims are not directed to one of the four statutory categories. As per claim 15 the claim is directed to “A computer program product, wherein the computer program product stores instructions, and when the instructions are executed by a processing device to perform the method of claim 20. The broadest reasonable interpretation of the term “computer program product” includes transitory signals. Claims which are directed to transitory signals are directed to patent ineligible subject matter. See In re Nuijten, 500 F.3d 1346, 84 USPQ2d 1495 (Fed. Cir. 2007). No other physical components are required by the claim. Thus, the claim is directed to subject matter which encompasses transitory signals within its broadest reasonable interpretation, and is thus not directed to patent eligible subject matter under 35 U.S.C. 101.
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.
The factual inquiries 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.
This application currently names joint inventors. In considering patentability of the claims the examiner presumes that the subject matter of the various claims was commonly owned as of the effective filing date of the claimed invention(s) absent any evidence to the contrary. Applicant is advised of the obligation under 37 CFR 1.56 to point out the inventor and effective filing dates of each claim that was not commonly owned as of the effective filing date of the later invention in order for the examiner to consider the applicability of 35 U.S.C. 102(b)(2)(C) for any potential 35 U.S.C. 102(a)(2) prior art against the later invention.
Claims 1-2, 14-16, 18, 20, and 25-26 are rejected under 35 U.S.C. 103 as being unpatentable over Repici, Dominic John (U.S. Patent No. 7,904,398 B1; hereinafter "Repici") in view of Venkatesh, et al. (U.S. PG Pub. No. 20210012202; hereinafter "Venkatesh").
As per claim 1, Repici teaches:
A data processing system, configured to have one or more system inputs comprising data to be processed and a system output, comprising:
Repici teaches a data processing system in the form of an ANN which takes in inputs and responds with outputs. (Repici: abstract)
a network comprising a plurality of nodes, each node being configured to have a plurality of inputs, each node comprising a weigh for each input, and each node configured to produce an output;
Repici teaches a network comprising a plurality of nodes in the form of an artificial neural network, wherein each nodes takes in an input and outputs a weighted output. (Repici: col. 2 lines 16-67, col. 3 lines 1-67 Figs. 1, 2)
Repici does not appear to explicitly teach:
and one or more processing units configured to receive a processing unit input and configured to produce a processing unit output by changing the sign of the received processing unit input;
Venkatesh, however, teaches a processing unit which may take in an input and produce an output by taking the absolute value of a negative input. (Venkatesh: paragraph [0054, 78]) It can be seen that each element is taught by either Repici or by Venkatesh. Adding the elements of Venkatesh to the teachings of Repici does not affect the normal functioning of the elements of the claim which are taught by Repici. Because the elements do not affect the normal functioning of each other, the results of their combination would have been predictable. Therefore, before the effective filing date of the claimed invention, it would have been obvious to combine the teachings of Venkatesh with the teachings of Repici, since the result is merely a combination of old elements, and, since the elements do not affect the normal functioning of each other, the results of the combination would have been predictable.
Repici in view of Venkatesh further teaches:
and wherein the system output comprises the outputs of each node,
Repici teaches a network comprising a plurality of nodes in the form of an artificial neural network, wherein each nodes takes in an input and outputs a weighted output. (Repici: col. 2 lines 16-67, col. 3 lines 1-67 Figs. 1, 2)
wherein nodes of a first group of the plurality of nodes are configured to excite one or more other nodes of the plurality of nodes by providing the output of each of the nodes of the first group of nodes as input to the one or more other nodes,
Repici teaches a network comprising a plurality of nodes in the form of an artificial neural network, wherein each nodes takes in an input and outputs a weighted output. (Repici: col. 2 lines 16-67, col. 3 lines 1-67 Figs. 1, 2) Repici teaches that a set of nodes may be present which produce a positive weight which may excite other nodes. (Repici: col. 3 lines 25-37, col. 14 lines 43-50)
wherein nodes of a second group of the plurality of nodes are configured to inhibit one or more other nodes of the plurality of nodes by providing the output of each of the nodes of the second group as a processing unit input to a respective processing unit, each respective processing unit being configured to provide the processing unit output as input to the one or more other nodes,
Repici teaches a network comprising a plurality of nodes in the form of an artificial neural network, wherein each nodes takes in an input and outputs a weighted output. (Repici: col. 2 lines 16-67, col. 3 lines 1-67 Figs. 1, 2) Repici teaches that a set of nodes may be present which produce a negative weight which may inhibit other nodes. (Repici: col. 3 lines 25-37, col. 15 lines 1-36)
wherein each node of the plurality of nodes belongs to one of the first and second groups of nodes,
Repici teaches a network comprising a plurality of nodes in the form of an artificial neural network, wherein each nodes takes in an input and outputs a weighted output. (Repici: col. 2 lines 16-67, col. 3 lines 1-67 Figs. 1, 2) Repici teaches that a set of nodes may be present which produce a negative weight which may inhibit other nodes. (Repici: col. 3 lines 25-37, col. 15 lines 1-36)
wherein each node comprises an updating unit, wherein each updating unit is configured to update the weights of the respective node based on correlation of each respective input of the node with the output of that node, and
Repici teaches that each node may comprise its own weight learning adjusting algorithm, wherein the weights attributed to that node may be adjusted and learned over time. (Repici: col. 20 lines 2-41)
wherein each updating unit is configured to apply a first function to the correlation if the associated node belongs to the first group of the plurality of nodes and apply a second function, different from the first function, to the correlation if the associated node belongs to the second group of the plurality of nodes in order to update the weights during the learning mode.
Repici teaches that each node may comprise its own weight learning adjusting algorithm, wherein the weights attributed to that node may be adjusted and learned over time. (Repici: col. 20 lines 2-41) Venkatesh further teaches that a weight in the form of a scaling factor may be applied, wherein a first function for applying the scaling factor may be used if the weight is a positive value (belongs to the first group) and a second function for applying the scaling factor may be applied if the weight attributed to a node is a negative value (belongs to the second group). (Venkatesh: paragraphs [0025, 54-57, 70, 78-81], Fig. ) The motivation to combine Venkatesh persists.
As per claim 2, Repici in view of Venkatesh teaches all of the limitations of claim 1, as outlined above, and further teaches:
wherein the one or more system inputs comprises sensor data of a plurality of contexts/tasks.
Repici teaches that the input into the nodes may comprise inputs from sensors. (Repici: col. 2 lines 23-26)
As per claim 14, Repici in view of Venkatesh teaches all of the limitations of claim 2, as outlined above, and further teaches:
wherein the data processing system is configured to from the sensor data learn to identify one or more entities while in a learning mode and thereafter configured to identify the one or more entities while in a performance mode.
Repici further teaches a learning phase and a task execution phase where a prediction is made. (Repici: col. 3 lines 37-52, co. 4 lines 64-67; col. 5 lines 1-10) Repici further teaches that the task may comprise character recognition. (Repici: col. 7 lines 17-33)
As per claim 15, Repici in view of Venkatesh teaches all of the limitations of claim 14, as outlined above, and further teaches:
wherein the identified entity is one or more of a speaker, a spoken letter, syllable, phoneme, word or phrase present in the sensor data.
Repici further teaches a learning phase and a task execution phase where a prediction is made. (Repici: col. 3 lines 37-52, co. 4 lines 64-67; col. 5 lines 1-10) Repici further teaches that the task may comprise character recognition. (Repici: col. 7 lines 17-33) Venkatesh further teaches that the neural network may be used for video or audio based natural language processing. (Venkatesh: paragraph [0026-28]) The motivation to combine Venkatesh persists.
As per claim 16, Repici in view of Venkatesh teaches all of the limitations of claim 14, as outlined above, and further teaches:
wherein the identified entity is an object or a feature of an object present in sensor data.
Repici further teaches a learning phase and a task execution phase where a prediction is made. (Repici: col. 3 lines 37-52, co. 4 lines 64-67; col. 5 lines 1-10) Repici further teaches that the task may comprise character recognition. (Repici: col. 7 lines 17-33) Venkatesh further teaches that the neural network may be used for object recognition in video. (Venkatesh: paragraph [0026-28]) The motivation to combine Venkatesh persists.
As per claim 18, Repici in view of Venkatesh teaches all of the limitations of claim 1, as outlined above, and further teaches:
wherein the network is a recurrent neural network.
Venkatesh further teaches that the neural network may comprise a recurrent neural network. (Venkatesh: paragraph [0026]) The motivation to combine Venkatesh persists.
As per claim 20, Repici teaches:
A computer implemented or hardware implemented method for processing data, comprising:
Repici teaches a data processing system in the form of an ANN which takes in inputs and responds with outputs. (Repici: abstract)
receiving one or more system inputs comprising data to be processed;
Repici teaches a network comprising a plurality of nodes in the form of an artificial neural network, wherein each nodes takes in an input and outputs a weighted output. (Repici: col. 2 lines 16-67, col. 3 lines 1-67 Figs. 1, 2)
providing a plurality of inputs, at least one of the plurality of inputs being a system input, to a network comprising a plurality of first nodes;
Repici teaches a network comprising a plurality of nodes in the form of an artificial neural network, wherein each nodes takes in an input and outputs a weighted output. (Repici: col. 2 lines 16-67, col. 3 lines 1-67 Figs. 1, 2)
receiving an output from each first node;
Repici teaches a network comprising a plurality of nodes in the form of an artificial neural network, wherein each nodes takes in an input and outputs a weighted output. (Repici: col. 2 lines 16-67, col. 3 lines 1-67 Figs. 1, 2)
providing a system output, comprising the output of each first node;
Repici teaches a network comprising a plurality of nodes in the form of an artificial neural network, wherein each nodes takes in an input and outputs a weighted output. (Repici: col. 2 lines 16-67, col. 3 lines 1-67 Figs. 1, 2)
exciting of a first group of the plurality of nodes, one or more other nodes of the plurality of nodes by providing the output of each of the nodes of the first group of nodes as input to the one or more other nodes;
Repici teaches a network comprising a plurality of nodes in the form of an artificial neural network, wherein each nodes takes in an input and outputs a weighted output. (Repici: col. 2 lines 16-67, col. 3 lines 1-67 Figs. 1, 2) Repici teaches that a set of nodes may be present which produce a positive weight which may excite other nodes. (Repici: col. 3 lines 25-37, col. 14 lines 43-50)
inhibiting, by nodes of a second group of the plurality of nodes, one or more other nodes of the plurality of nodes by providing the output of each of the nodes of the second group as a processing unit input to a respective processing unit, each respective processing unit being configured to provide the processing unit output as input to the one or more other nodes;
Repici teaches a network comprising a plurality of nodes in the form of an artificial neural network, wherein each nodes takes in an input and outputs a weighted output. (Repici: col. 2 lines 16-67, col. 3 lines 1-67 Figs. 1, 2) Repici teaches that a set of nodes may be present which produce a negative weight which may inhibit other nodes. (Repici: col. 3 lines 25-37, col. 15 lines 1-36)
With respect to the following limitation:
and updating, for each node, the weights based on correlation of each respective input f the node with the output of that node and applying a first function to the correlation if the associated node belongs to the first group of the plurality of nodes and apply a second function, different from the first function, to the correlation if the associated node belongs to the second group of the plurality of nodes in order to update the weights during the learning mode, wherein each node of the plurality of nodes belongs to one of the first and second groups of nodes.
Repici teaches that each node may comprise its own weight learning adjusting algorithm, wherein the weights attributed to that node may be adjusted and learned over time. (Repici: col. 20 lines 2-41) Repici, however, does not appear to explicitly teach that the weights are updated according to a different function based on whether they belong to a first group or second group.
Venkatesh further teaches that a weight in the form of a scaling factor may be applied, wherein a first function for applying the scaling factor may be used if the weight is a positive value (belongs to the first group) and a second function for applying the scaling factor may be applied if the weight attributed to a node is a negative value (belongs to the second group). (Venkatesh: paragraphs [0025, 54-57, 70, 78-81], Fig. ) Adding the elements of Venkatesh to the teachings of Repici does not affect the normal functioning of the elements of the claim which are taught by Repici. Because the elements do not affect the normal functioning of each other, the results of their combination would have been predictable. Therefore, before the effective filing date of the claimed invention, it would have been obvious to combine the teachings of Venkatesh with the teachings of Repici, since the result is merely a combination of old elements, and, since the elements do not affect the normal functioning of each other, the results of the combination would have been predictable.
As per claim 25, Repici in view of Venkatesh teaches all of the limitations of claim 20, as outlined above, and further teaches:
A computer program product comprising instructions, which, when executed on at least one processor of a processing device, cause the processing device to carry out the method according to claim 20.
Repici teaches a data processing system in the form of an ANN which takes in inputs and responds with outputs. (Repici: abstract) Venkatesh further teaches the implementation of the system and method using a computer which executes code stores in a physical memory in order to perform the functions of the system. (Venkatesh: paragraphs [0048-53], Fig. 1D)
As per claim 26, Repici in view of Venkatesh teaches all of the limitations of claim 20, as outlined above, and further teaches:
A non-transitory computer-readable storage medium storing one or more programs configured to be executed by one or more processors of a processing device, the one or more programs comprising instructions which, when executed by the processing device, causes the processing device to carry out the method according to claim 20.
Repici teaches a data processing system in the form of an ANN which takes in inputs and responds with outputs. (Repici: abstract) Venkatesh further teaches the implementation of the system and method using a computer which executes code stores in a physical memory in order to perform the functions of the system. (Venkatesh: paragraphs [0048-53], Fig. 1D)
Claims 17, 19, and 21-22 are rejected under 35 U.S.C. 103 as being unpatentable over Repici in view of Venkatesh further in view of Zhou et al. (WIP Patent Document No. WO 2022103412 A1; hereinafter "Zhou").
As per claim 17, Repici in view of Venkatesh teaches all of the limitations of claim 14, as outlined above, but does not appear to explicitly teach:
wherein the identified entity is a new contact event, an end of a contact event, a gesture or an applied pressure present in the sensor data.
Zhou, however, teaches that a recursive neural network may be used to take in inputs and output recognition of gestures. (Zhou: paragraphs [0035-40]) Zhou teaches combining the above elements with the teachings of Repici in view of Venkatesh for the benefit of allowing repeated and continuous usage of air swipe gestures, allowing a user to quickly browse e-books and photos, and allowing continuous control in one direction via air swipe gestures while ensuring that a system will not mistakenly misclassify a gesture. (Zhou: pages 35-36) Therefore, before the effective filing date of the claimed invention, it would have been obvious to one of ordinary skill in the art to combine the teachings of Zhou with the teachings of Repici in view of Venkatesh to achieve the aforementioned benefits.
As per claim 19, Repici in view of Venkatesh teaches all of the limitations of claim 11, as outlined above, but does not appear to explicitly teach:
wherein the network is a recursive neural network.
Zhou, however, teaches that a recursive neural network may be used to take in inputs and output recognition of gestures. (Zhou: paragraphs [0035-40]) Zhou teaches combining the above elements with the teachings of Repici in view of Venkatesh for the benefit of allowing repeated and continuous usage of air swipe gestures, allowing a user to quickly browse e-books and photos, and allowing continuous control in one direction via air swipe gestures while ensuring that a system will not mistakenly misclassify a gesture. (Zhou: pages 35-36) Therefore, before the effective filing date of the claimed invention, it would have been obvious to one of ordinary skill in the art to combine the teachings of Zhou with the teachings of Repici in view of Venkatesh to achieve the aforementioned benefits.
As per claim 21, Repici in view of Venkatesh teaches all of the limitations of claim 20, as outlined above, but does not appear to explicitly teach:
repeating the steps of receiving one or more system inputs, providing a plurality of inputs, receiving an output, providing a system output, exciting, inhibiting, and updating until a learning criterion is met.
Zhou, however, teaches that a recursive neural network may be used to take in inputs and output recognition of gestures. (Zhou: paragraphs [0035-40]) Zhou further teaches that the neural network may be iteratively trained until a learning criterion and/or a stop criterion in the form of a loss criteria, loss minimization, or loss threshold is met. (Zhou: paragraph [0030]) Zhou teaches combining the above elements with the teachings of Repici in view of Venkatesh for the benefit of allowing repeated and continuous usage of air swipe gestures, allowing a user to quickly browse e-books and photos, and allowing continuous control in one direction via air swipe gestures while ensuring that a system will not mistakenly misclassify a gesture. (Zhou: pages 35-36) Therefore, before the effective filing date of the claimed invention, it would have been obvious to one of ordinary skill in the art to combine the teachings of Zhou with the teachings of Repici in view of Venkatesh to achieve the aforementioned benefits.
As per claim 22, Repici in view of Venkatesh teaches all of the limitations of claim 20, as outlined above, but does not appear to explicitly teach:
repeating the steps of receiving one or more system inputs, providing a plurality of inputs, receiving an output, providing a system output, exciting and inhibiting until a stop criterion is met.
Zhou, however, teaches that a recursive neural network may be used to take in inputs and output recognition of gestures. (Zhou: paragraphs [0035-40]) Zhou further teaches that the neural network may be iteratively trained until a learning criterion and/or a stop criterion in the form of a loss criteria, loss minimization, or loss threshold is met. (Zhou: paragraph [0030]) Zhou teaches combining the above elements with the teachings of Repici in view of Venkatesh for the benefit of allowing repeated and continuous usage of air swipe gestures, allowing a user to quickly browse e-books and photos, and allowing continuous control in one direction via air swipe gestures while ensuring that a system will not mistakenly misclassify a gesture. (Zhou: pages 35-36) Therefore, before the effective filing date of the claimed invention, it would have been obvious to one of ordinary skill in the art to combine the teachings of Zhou with the teachings of Repici in view of Venkatesh to achieve the aforementioned benefits.
Conclusion
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/EMMETT K. WALSH/Primary Examiner, Art Unit 3626
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