DETAILED ACTION
The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA .
Continued Examination Under 37 CFR 1.114
A request for continued examination under 37 CFR 1.114, including the fee set forth in 37 CFR 1.17(e), was filed in this application after final rejection. Since this application is eligible for continued examination under 37 CFR 1.114, and the fee set forth in 37 CFR 1.17(e) has been timely paid, the finality of the previous Office action has been withdrawn pursuant to 37 CFR 1.114. Applicant's submission filed on 01/20/26 has been entered.
Response to Arguments
35 USC 112(b). The rejection of claims 1-14, 16-17, and 20-34 is withdrawn based on amendment to claims.
35 USC 101.
Applicant asserts that the claims as amended add meaningful limitations beyond generally linking the use of the alleged judicial exception to a particular technological environment, citing that the claims are analogous to Diamond v Diehr. Applicant asserts that the claims have been amended to sufficiently limit the use of the alleged mathematical concepts to practical applications of automatic speech recognition, computer vision and object recognition (Remarks p. 11-12).
Examiner respectfully disagrees. The claims as amended are more like Parker v Flook, wherein the claims are generically pre-pended or appended to recite application to a particular field of use or technological environment. The claims are unlike Diamond v Diehr wherein the additional elements comprised elements integral to the claim, such as initiating a timer, constantly determining a temperature of a mold, constantly providing the computing with the temperature, repetitively comparing in the computer at frequent time intervals, and opening the press. The instant claims are devoid of elements integral to the claim beyond the general linking.
Applicant further asserts the claim is required to perform O(T hat M’ hat M’’ hat) or fewer operations when decoding the known SSM model, and that this results in an improvement in technology (remarks p. 12).
Examiner respectfully disagrees. The number of operations required is a direct result of the mathematical calculations and not as a result in an improvement in technology per se.
Claim Interpretation
Claim element “SSM model” is defined as the matrix M and the vectors h’ and h’’ for a sequence pair (S’, S’’) that are computed by an encoding algorithm when encoding, and the matrix M and the vector h’’ that are computed by the encoding algorithm when decoding, and by default the SSM model refers to the decoding model of figure 12, as stated in [0218] second to last sentence. See also [0214-0218], and figures 11-13.
Drawings
The drawings are objected to under 37 CFR 1.83(a). The drawings must show every feature of the invention specified in the claims. Therefore, the input device for receiving a visual image as in claim 9, an input device for receiving a data input, a processor coupled to the input device, and a memory device configured to store a plurality of known SSM models as in claim 13, and an input device of a robot configured to receive sensorimotor modalities, a processor coupled to the input device, and a memory device configured to store a plurality of previously encoded SSM models as in claim 22 must be shown or the features canceled from the claims. No new matter should be entered.
Corrected drawing sheets in compliance with 37 CFR 1.121(d) are required in reply to the Office action to avoid abandonment of the application. Any amended replacement drawing sheet should include all of the figures appearing on the immediate prior version of the sheet, even if only one figure is being amended. The figure or figure number of an amended drawing should not be labeled as “amended.” If a drawing figure is to be canceled, the appropriate figure must be removed from the replacement sheet, and where necessary, the remaining figures must be renumbered and appropriate changes made to the brief description of the several views of the drawings for consistency. Additional replacement sheets may be necessary to show the renumbering of the remaining figures. Each drawing sheet submitted after the filing date of an application must be labeled in the top margin as either “Replacement Sheet” or “New Sheet” pursuant to 37 CFR 1.121(d). If the changes are not accepted by the examiner, the applicant will be notified and informed of any required corrective action in the next Office action. The objection to the drawings will not be held in abeyance.
Claim Rejections - 35 USC § 112
The following is a quotation of the first paragraph of 35 U.S.C. 112(a):
(a) IN GENERAL.—The specification shall contain a written description of the invention, and of the manner and process of making and using it, in such full, clear, concise, and exact terms as to enable any person skilled in the art to which it pertains, or with which it is most nearly connected, to make and use the same, and shall set forth the best mode contemplated by the inventor or joint inventor of carrying out the invention.
The following is a quotation of the first paragraph of pre-AIA 35 U.S.C. 112:
The specification shall contain a written description of the invention, and of the manner and process of making and using it, in such full, clear, concise, and exact terms as to enable any person skilled in the art to which it pertains, or with which it is most nearly connected, to make and use the same, and shall set forth the best mode contemplated by the inventor of carrying out his invention.
Claims 1-14, 16-17, and 20-34 are rejected under 35 U.S.C. 112(a) or 35 U.S.C. 112 (pre-AIA ), first paragraph, as failing to comply with the written description requirement. The claim(s) contains subject matter which was not described in the specification in such a way as to reasonably convey to one skilled in the relevant art that the inventor or a joint inventor, or for applications subject to pre-AIA 35 U.S.C. 112, the inventor(s), at the time the application was filed, had possession of the claimed invention.
Regarding claim 1, claim 1 recites receiving a first collection of signals B hat representing an audio input via an input device, and scaling, decoding, matching and selecting steps performed by the processor with respect to the first collection of signals B hat. The specification, however, only generally refers to applications to a variety of fields of use in which the models and algorithms disclosed can be used including speech and sound ([0012]), without explicitly disclosing wherein a first collection of signals B hat representing an audio input are received via an input device. Furthermore, the specification provides no disclosure of the processor. Claims 2-8 inherit the same deficiency as claim 1 based on dependence.
Regarding claim 9, claim 9 recites an input device for receiving a visual image, and a processor coupled to the input device, the processor configured to convert the visual image into a first collection of signals A hat and a second collection of signals B hat … as each of the at least one signal in each of the A hat and B hat are received by the processor, a memory device configured to store a plurality of known SSM models, and wherein the processor is configured to decode at least one known SSM model … to match the visual image to a subset of the previously encoded visual image represented by the plurality of known SSM models. The specification, however, only generally refers to applications to a variety of fields of use in which the models and algorithms disclosed can be used including computer vision ([0012]), without explicitly disclosing an input device for receiving a visual image, and a processor coupled to the input device, the processor configured to convert the visual image into a first collection of signals A hat and a second collection of signals B hat, and a memory device configured to store. Claims 10-12, 16-17, 20-21, and 24-34 inherit the same deficiency as claim 9 based on dependence.
Regarding claim 13, claim 13 recites an input device for receiving a data input, a processor coupled to the input device, the processor configured to convert the data input into a first collection of signals A hat and a second collection of signals B hat, and the processor configured to continuously scale at least one signa in each of A hat and B hat … as each of the at least one signal in each of A hat and B hat are received by the processor, a memory device configured to store a plurality of known SSM models. The specification, however provides no disclosure of the processor, input device and memory device as configured in claim 13. Claims 14 and 23 inherit the same deficiency as claim 9 based on dependence.
Regarding claim 22, claim 22 recites an interactive object recognition system for a robot, comprising an input device of the robot configured to receive sensorimotor modalities while the robot performs at least one exploratory behavior on at least on object, a processor coupled to the input device, the processor configured to convert the sensorimotor modalities into a first collection of signals A hat and a second collection of signals B hat, and the processor configured to continuously scale at least one signa in each of A hat and B hat … as each of the at least one signal in each of A hat and B hat are received by the processor, a memory device configured to store a plurality of encoded SSM models … wherein the processor is configured to decode at least one known SSM model using the second collection of signals B hat and the processor is configured to match the sensorimotor modalities to a subset of the known objects represented by the plurality of previously encoded SSM models. The specification, however, only generally refers to applications to a variety of fields of use in which the models and algorithms disclosed can be used including robotics ([0003], [0012]), without explicitly disclosing Furthermore, the specification provides no disclosure of the processor, input device and memory device as configured in claim 22.
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.
Claims 1-14, 16-17, and 20-34 are rejected under 35 U.S.C. 101 because the claimed invention is directed to a judicial exception (i.e., a law of nature, a natural phenomenon, or an abstract idea) without significantly more.
Apparatus claims will be addressed first, followed by the method claims.
Regarding claim 9 under the Alice Framework Step 1 analysis, the claim falls within the four statutory categories of patentable subject matter: an apparatus.
Under the Alice Framework Step 2A prong 1, the claim recites Mathematical Concepts. Claim 9 recites mathematical steps of converting data, scaling using a weighting function, decoding at least one SSM model and matching data to SSM models using mathematical relationships and mathematical calculations. Specifically the mathematical concepts recited include:
convert the visual image into a first collection of signals A and a second collection of signals B and to continuously scale at least one signal in each of A and B using a weighting function that is not always equal to 1;
SSM models representing a plurality of previously encoded data inputs;
decode at least one known SSM model using the scaled second collection of signals B and match the visual image to a subset of the previously encoded visual image represented by the plurality of known SSM models.
These elements are described throughout the specification with respect to equations, mathematical relationships and mathematical calculations. See e.g. scaling with weighting in decoding, encoded models as in [0557], [0782-0785], [0788-0791], [0794], [0798], [0804-0807], [0813], [0827-0838], [0864-0868], [0885-0896]. See also with respect to continuously, as in continuous scaling this is described mathematically in terms of continuous functions with respect to time as in [0791} and 9.10, weighting function for scaling. Furthermore the matching limitations, to match the visual image to a subset of the previously encoded visual image represented by the plurality of known SSM models are disclosed as in correlation functions. See for example [0792-0804]. Furthermore, with respect to decoding at least one SSM model, this describes mathematical relationships. As defined by Merriam-Webster, a model is a “representation of something” and “a system of postulates, data, and inferences presented as a mathematical description of an entity”. See https://www.merriam-webster.com/dictionary/model. Furthermore the specification defines the “SSM model” in terms of a mathematical description of an entity, i.e., as the matrix M and the vectors h’ and h’’ for a sequence pair (S’, S’’) that are computed by an encoding algorithm when encoding, and the matrix M and the vector h’’ that are computed by the encoding algorithm when decoding. See [0214-0218], and figures 11-13. The encoding and decoding is described in the specification in terms of applying algorithms, i.e., encoding or decoding algorithms to the SSM model for a sequence pair S’ and S” as shown in figures 12 and 14, [0218-0222] (decoding), and 10, and 26 [0216, [0250-0253] (encoding). These sections describe the encoding and decoding in terms of mathematical relationships.
Under the Alice Framework Step 2A, prong 2 analysis, the claims recite the following additional elements: a computer vision system, comprising: an input device for receiving a visual image, a processor coupled to the input device and configured, receiving by the processor, and a memory device configured to store, and the converting and scaling being of a first and second collection of signals A hat and B hat. The input device, processor and memory device are merely generally linked to the mathematical relationships and mathematical calculations in a manner that merely “apply it” on a computer. Furthermore, the receiving a visual image, receiving by the processor, and storing in a memory device comprise insignificant extra solution activity. Furthermore, the recitation of the claimed first and second collection of signals in this manner merely generally links or “applies” the mathematical concepts to a particular technological environment, signals in a manner that the claim as a whole is no more than a drafting effort designed to monopolize the exception. In fact application of the claimed invention to “signals” is given only cursory description in the background summary of the invention. See [0002 – 0012]. The remainder of the disclosure describes the claimed invention in terms of application of the SSM model to encoding and decoding of sequences of numbers or letters, i.e., mathematical relationships. No use of the mathematical concepts in performing the methods in conjunction with a particular machine or manufacture integral to the claim is claimed beyond mathematical concepts applied to “signals”. Furthermore the preamble reciting a computer vision system, and a visual image merely generally links the mathematical concepts to a particular technological environment or field of use. For these reasons, the claim is not integrated into a practical application.
Under the Step 2B analysis, for at least the reasons cited in the step 2A prong 2 analysis, the claims when considered as a whole do not amount to significantly more than the abstract idea. What is novel is in the mathematical concepts, i.e. use of the SSM model to convert and represent encodings, scale with weighting functions, decode the SSM model, and match to models. The claims merely generally link, or “apply” the math to collections of signals and “apply it” as to the abstract idea in a computer. Furthermore the Input device for receiving a data input, a processor coupled to the input device, a memory device configured to store and receiving comprise well understood, routine, and conventional activity. See D.A Patterson, et al., Computer Organization and Design: The Hardware/Software Interface, Elsevier Science & Technology, 2007 Ch 1 and figure 1. 5 (hereinafter “Patterson”), describing the five classic components of the computer including processor , memory device storing data, input receiving data, and processor coupled to the input device. Furthermore the preamble reciting a computer vision system, and a visual image merely generally links the mathematical concepts to a particular technological environment or field of use. For this reason the claim does not amount to significantly more than the abstract idea.
Claims 10-12, 16-17, 20-21, and 24-34 are rejected for at least the reasons cited with respect to claim 9. Under Step 2A prong 1, claims 10-11, and 16 merely further mathematically limit the apparatus of claim 9.
Claims 10-11, and 16 recite no further additional elements that would require further analysis under Step 2A prong 2 and Step 2B.
Claim 12 further mathematically limits the apparatus of claim 9. Under the step 2A prong 2 and step 2B, claim 12 recites additional mathematical concepts associated with additional elements of the data input, memory device storing, and further signals. The analysis provided with respect to claim 9 applies equally to claim 12 as to both steps 2A prong 2 and step 2B.
Claims 17, and 20-21 under the step 2A prong 2 and step 2B analysis merely generally link the mathematical concepts to a particular technological environment or field of use. No specific limitation of these additional elements particularly limit the abstract idea within these fields of use or technological environment is claimed beyond the general linking.
Claim 24 further mathematically limits the apparatus of claim 9. Under the step 2A prong 2, claim 24 recites the additional element of selecting the next data input based on the result of the mathematical concepts. This limitation comprises an insignificant extra solution activity. Under the step 2B analysis this limitation is well understood, routine and conventional. See Patterson, figure 1.5, which shows the datapath and control selecting inputs processed by the processor.
Claim 25 recites the additional element wherein the system is configured to be used as an associative memory. Under the step 2A prong 2 and step 2B analysis, this limitation merely generally links the mathematical concepts to a particular technological environment without specific limitation to particularly limit the abstract idea within the associative memory.
Claim 26 under the step 2A prong 2 and step 2B analysis merely generally link the mathematical concepts to a particular field of use. No specific limitation of these additional elements particularly limit the abstract idea within this fields of use is claimed beyond the general linking.
Claim 27 under the step 2A prong 2 analysis includes the processor comprises a plurality of parallel processors. This element is merely generally linked to a computer comprising parallel processors in a manner that merely “applies it” in a parallel processor computer. Under the step 2B analysis the computer comprising a plurality of parallel processors is well-understood, routine and conventional. See, e.g., JL Hennessy et al., Computer architecture: A quantitative approach, Elsevier Science & Technology, 2014, p. 527-548, 608-614 (hereinafter “Hennessy”) which describes numerous parallel architectures that have been described for over 30 years, and that provided for parallel, distributed and replicated operation of computing systems including application to scientific calculations including matrix calculations.
Under Step 2A prong 1, claims 28-31, and claims 33-34 merely further mathematically limit the apparatus of claim 9. Claims 28-31, and claims 33-34 recite no further additional elements that would require further analysis under Step 2A prong 2 and Step 2B.
Claim 32 recites “the first vector h’ is not stored after completing encoding” (emphasis added). This negative limitation is not being considered an additional element, because as a result of the negative limitation, there is no element to consider for integration into a practical application or for significantly more than the abstract idea. Furthermore any lack of need to store the vector h’ flows as a direct consequence of the mathematical relationships and calculations in the encoding algorithm. For these reasons, the claim recites mathematical concepts under step 2A prong 1, and contains no further additional elements that would require further consideration under step 2A prong 2 and step 2B.
Regarding claim 13 under the Alice Framework Step 1 analysis, the claim falls within the four statutory categories of patentable subject matter: an apparatus.
Under the Alice Framework Step 2A prong 1, the claim recites Mathematical Concepts. Claim 13 recites mathematical steps of converting data, scaling using a weighting function, decoding at least one SSM model and matching data to SSM models using mathematical relationships and mathematical calculations. Specifically the mathematical concepts recited include:
convert the data input into a first collection of signals A and a second collection of signals B and to continuously scale at least one signal in each of A and B using a weighting function that is not always equal to 1;
SSM models representing a plurality of previously encoded data inputs;
decode at least one known SSM model using the scaled second collection of signals B and match the data input to a subset of the plurality of known SSM models.
These elements are described throughout the specification with respect to equations, mathematical relationships and mathematical calculations. See e.g. scaling with weighting in decoding, encoded models as in [0557], [0782-0785], [0788-0791], [0794], [0798], [0804-0807], [0813], [0827-0838], [0864-0868], [0885-0896]. See also with respect to continuously, as in continuous scaling this is described mathematically in terms of continuous functions with respect to time as in [0791} and 9.10, weighting function for scaling. Furthermore the matching limitations, to match the data input to a subset of the plurality of known SSM models are disclosed as in correlation functions. See for example [0792-0804]. Furthermore, with respect to decoding at least one SSM model, this describes mathematical relationships. As defined by Merriam-Webster, a model is a “representation of something” and “a system of postulates, data, and inferences presented as a mathematical description of an entity”. See https://www.merriam-webster.com/dictionary/model. Furthermore the specification defines the “SSM model” in terms of a mathematical description of an entity, i.e., as the matrix M and the vectors h’ and h’’ for a sequence pair (S’, S’’) that are computed by an encoding algorithm when encoding, and the matrix M and the vector h’’ that are computed by the encoding algorithm when decoding. See [0214-0218], and figures 11-13. The encoding and decoding is described in the specification in terms of applying algorithms, i.e., encoding or decoding algorithms to the SSM model for a sequence pair S’ and S” as shown in figures 12 and 14, [0218-0222] (decoding), and 10, and 26 [0216, [0250-0253] (encoding). These sections describe the encoding and decoding in terms of mathematical relationships.
Under the Alice Framework Step 2A, prong 2 analysis, the claims recite the following additional elements: a system, comprising: an input device for receiving a data input, a processor coupled to the input device and configured, receiving by the processor, and a memory device configured to store, and the converting and scaling being of a first and second collection of signals A hat and B hat, and wherein the processor performs O(T hat, M’ hat, M’’ hat) or fewer primitive operations when decoding a known SSM model, wherein T hat is the length of the second sequence S’’ primed, M’ hat is the number of signals in the first collection of signals A hat, and M’’ hat is the number of signals in the second collection of signals B hat. The input device, processor and memory device are merely generally linked to the mathematical relationships and mathematical calculations in a manner that merely “apply it” on a computer. Furthermore, the receiving data input, receiving by the processor, and storing in a memory device comprise insignificant extra solution activity. Furthermore, the recitation of the claimed first and second collection of signals in this manner merely generally links or “applies” the mathematical concepts to a particular technological environment, signals in a manner that the claim as a whole is no more than a drafting effort designed to monopolize the exception. In fact application of the claimed invention to “signals” is given only cursory description in the background summary of the invention. See [0002 – 0012]. The remainder of the disclosure describes the claimed invention in terms of application of the SSM model to encoding and decoding of sequences of numbers or letters, i.e., mathematical relationships. No use of the mathematical concepts in performing the methods in conjunction with a particular machine or manufacture integral to the claim is claimed beyond mathematical concepts applied to “signals”. Furthermore the limitation wherein the processor performs O(T hat, M’ hat, M’’ hat) or fewer primitive operations, merely recites an intended result of the mathematical concepts, wherein the number of operations performed flows as a direct result of the math. For these reasons, the claim is not integrated into a practical application.
Under the Step 2B analysis, for at least the reasons cited in the step 2A prong 2 analysis, the claims when considered as a whole do not amount to significantly more than the abstract idea. What is novel is in the mathematical concepts, i.e. use of the SSM model to convert and represent encodings, scale with weighting functions, decode the SSM model, and match to models. The claims merely generally link, or “apply” the math to collections of signals and “apply it” as to the abstract idea in a computer. Furthermore the Input device for receiving a data input, a processor coupled to the input device, a memory device configured to store and receiving comprise well understood, routine, and conventional activity. See Patterson Ch 1 and figure 1.5 , describing the five classic components of the computer including processor , memory device storing data, input receiving data, and processor coupled to the input device. Furthermore the limitation wherein the processor performs O(T hat, M’ hat, M’’ hat) or fewer primitive operations, merely recites an intended result of the mathematical concepts, wherein the number of operations performed flows as a direct result of the math. For this reason the claim does not amount to significantly more than the abstract idea.
Claims 14 and 23 are rejected for at least the reasons cited with respect to claim 13. Under Step 2A prong 1, claim 14 merely further mathematically limit the apparatus of claim 13. Claim 14 recites no further additional elements that would required further analysis under Step 2A prong 2 or Step 2B.
Claim 23 under the step 2A prong 2 and step 2B analysis merely generally link the mathematical concepts to a particular field of use. No specific limitation of these additional elements particularly limit the abstract idea within this fields of use is claimed beyond the general linking.
Regarding claim 22 under the Alice Framework Step 1 analysis, the claim falls within the four statutory categories of patentable subject matter: an apparatus.
Under the Alice Framework Step 2A prong 1, the claim recites Mathematical Concepts. Claim 22 recites mathematical steps of converting data, scaling using a weighting function, decoding at least one SSM model and matching data to SSM models using mathematical relationships and mathematical calculations. Specifically the mathematical concepts recited include:
convert the sensorimotor modalities into a first collection of signals A and a second collection of signals B and to continuously scale at least one signal in each of A and B using a weighting function that is not always equal to 1;
previously encoded SSM models representing a plurality of known objects;
decode at least one known SSM model using the scaled second collection of signals B and match the sensorimotor modalities to a subset of known objects represented by the plurality of previously encoded SSM models, and wherein the subset of known objects is selected based on the outcomes of the decoding.
These elements are described throughout the specification with respect to equations, mathematical relationships and mathematical calculations. See e.g. scaling with weighting in decoding, encoded models as in [0557], [0782-0785], [0788-0791], [0794], [0798], [0804-0807], [0813], [0827-0838], [0864-0868], [0885-0896]. See also with respect to continuously, as in continuous scaling this is described mathematically in terms of continuous functions with respect to time as in [0791} and 9.10, weighting function for scaling. Furthermore the matching limitations, to match the sensorimotor modalities to a subset of the known objects represented by the plurality of previously encoded SSM models comprise correlation functions. See for example [0792-0804]. Furthermore, with respect to decoding at least one SSM model, this describes mathematical relationships. As defined by Merriam-Webster, a model is a “representation of something” and “a system of postulates, data, and inferences presented as a mathematical description of an entity”. See https://www.merriam-webster.com/dictionary/model. Furthermore the specification defines the “SSM model” in terms of a mathematical description of an entity, i.e., as the matrix M and the vectors h’ and h’’ for a sequence pair (S’, S’’) that are computed by an encoding algorithm when encoding, and the matrix M and the vector h’’ that are computed by the encoding algorithm when decoding. See [0214-0218], and figures 11-13. The encoding and decoding is described in the specification in terms of applying algorithms, i.e., encoding or decoding algorithms to the SSM model for a sequence pair S’ and S” as shown in figures 12 and 14, [0218-0222] (decoding), and 10, and 26 [0216, [0250-0253] (encoding). These sections describe the encoding and decoding in terms of mathematical relationships. Furthermore, the selecting is a result of the mathematical concepts.
Under the Alice Framework Step 2A, prong 2 analysis, the claims recite the following additional elements: an object recognition system for a robot, comprising: an input device of the robot configured to receive sensorimotor modalities while the robot performs at least one exploratory behavior, a processor coupled to the input device and configured, receiving by the processor, and a memory device configured to store, and the converting and scaling being of a first and second collection of signals A hat and B hat. The input device, processor and memory device are merely generally linked to the mathematical relationships and mathematical calculations in a manner that merely “apply it” on a computer. Furthermore, the receiving sensorimotor modalities, receiving by the processor, and storing in a memory device comprise insignificant extra solution activity. Furthermore, the recitation of the claimed first and second collection of signals in this manner merely generally links or “applies” the mathematical concepts to a particular technological environment, signals in a manner that the claim as a whole is no more than a drafting effort designed to monopolize the exception. In fact application of the claimed invention to “signals” is given only cursory description in the background summary of the invention. See [0002 – 0012]. The remainder of the disclosure describes the claimed invention in terms of application of the SSM model to encoding and decoding of sequences of numbers or letters, i.e., mathematical relationships. No use of the mathematical concepts in performing the methods in conjunction with a particular machine or manufacture integral to the claim is claimed beyond mathematical concepts applied to “signals”. Furthermore the preamble reciting an interactive object recognition system for a robot, comprising an input device of the robot merely generally links the mathematical concepts to a particular technological environment or field of use. For these reasons, the claim is not integrated into a practical application.
Under the Step 2B analysis, for at least the reasons cited in the step 2A prong 2 analysis, the claims when considered as a whole do not amount to significantly more than the abstract idea. What is novel is in the mathematical concepts, i.e. use of the SSM model to convert and represent encodings, scale with weighting functions, decode the SSM model, and match to models. The claims merely generally link, or “apply” the math to collections of signals and “apply it” as to the abstract idea in a computer. Furthermore the Input device of the robot configured to receive, a processor coupled to the input device, a memory device configured to store and receiving comprise well understood, routine, and conventional activity. See Patterson Ch 1 and figure 1.5, describing the five classic components of the computer including processor , memory device storing data, input receiving data, and processor coupled to the input device. Furthermore the preamble reciting an interactive object recognition system for a robot, comprising an input device of the robot merely generally links the mathematical concepts to a particular technological environment or field of use. For this reason the claim does not amount to significantly more than the abstract idea.
Regarding claim 1 under the Alice Framework Step 1 analysis, the claim falls within the four statutory categories of patentable subject matter: a method.
Under the Alice Framework Step 2A prong 1, the claim recites Mathematical Concepts. Claim 1 recites mathematical steps of converting data, scaling using a weighting function, decoding at least one SSM model and matching data to SSM models using mathematical relationships and mathematical calculations. Specifically the mathematical concepts recited include:
continuously scaling at least one signal b hat of the first collection of signals B hat, the continuously scaling using at least one weighting function v hat selected from a fist collection weighting functions V hat, wherein v hat is not always equal to 1;
decoding a plurality of previously encoded SSM models using the first collection of signals B hat,
matching the first collection of signals B hat to a subset of the plurality of previously encoded SSM models based on the decoding, and
selecting a subset of known words based on the decoding and matching;
These elements are described throughout the specification with respect to equations, mathematical relationships and mathematical calculations. See e.g. scaling with weighting in decoding, encoded models as in [0557], [0782-0785], [0788-0791], [0794], [0798], [0804-0807], [0813], [0827-0838], [0864-0868], [0885-0896]. See also with respect to continuously, as in continuous scaling this is described mathematically in terms of continuous functions with respect to time as in [0791} and 9.10, weighting function for scaling. Furthermore the matching limitations comprise correlation functions. See for example [0792-0804]. Furthermore, with respect to decoding at least one SSM model, this describes mathematical relationships. As defined by Merriam-Webster, a model is a “representation of something” and “a system of postulates, data, and inferences presented as a mathematical description of an entity”. See https://www.merriam-webster.com/dictionary/model. Furthermore the specification defines the “SSM model” in terms of a mathematical description of an entity, i.e., as the matrix M and the vectors h’ and h’’ for a sequence pair (S’, S’’) that are computed by an encoding algorithm when encoding, and the matrix M and the vector h’’ that are computed by the encoding algorithm when decoding. See [0214-0218], and figures 11-13. The encoding and decoding is described in the specification in terms of applying algorithms, i.e., encoding or decoding algorithms to the SSM model for a sequence pair S’ and S” as shown in figures 12 and 14, [0218-0222] (decoding), and 10, and 26 [0216, [0250-0253] (encoding). These sections describe the encoding and decoding in terms of mathematical relationships. Furthermore, the selecting is a result of the mathematical concepts.
Under the Alice Framework Step 2A, prong 2 analysis, the claims recite the following additional elements: automatic speech recognition, comprising: receiving a first collection of signals B hat representing an audio input via an input device, steps performed by a processor. The input device and processor are merely generally linked to the mathematical relationships and mathematical calculations in a manner that merely “apply it” on a computer. Furthermore, receiving a first collection of signals B hat representing an audio input via an input device comprise insignificant extra solution activity. Furthermore the preamble reciting a method of automatic speech recognition merely generally links the mathematical concept to a field of use or technological environment. Furthermore, the recitation of the claimed first and second collection of signals in this manner merely generally links or “applies” the mathematical concepts to a particular technological environment, signals in a manner that the claim as a whole is no more than a drafting effort designed to monopolize the exception. In fact application of the claimed invention to “signals” is given only cursory description in the background summary of the invention. See [0002 – 0012]. The remainder of the disclosure describes the claimed invention in terms of application of the SSM model to encoding and decoding of sequences of numbers or letters, i.e., mathematical relationships. No use of the mathematical concepts in performing the methods in conjunction with a particular machine or manufacture integral to the claim is claimed beyond mathematical concepts applied to “signals”. For these reasons, the claim is not integrated into a practical application.
Under the Step 2B analysis, for at least the reasons cited in the step 2A prong 2 analysis, the claims when considered as a whole do not amount to significantly more than the abstract idea. What is novel is in the mathematical concepts, i.e. use of the SSM model to convert and represent encodings, scale with weighting functions, decode the SSM model, and match to models. The claims merely generally link, or “apply” the math to collections of signals and “apply it” as to the abstract idea in a computer. Furthermore the Input device of the robot configured to receive, a processor coupled to the input device, a memory device configured to store and receiving comprise well understood, routine, and conventional activity. See Patterson Ch 1 and figure 1.5, describing the five classic components of the computer including processor , memory device storing data, input receiving data, and processor coupled to the input device. Furthermore the preamble reciting a method of automatic speech recognition merely generally links the mathematical concepts to a particular technological environment or field of use. For this reason the claims do not amount to significantly more than the abstract idea.
Claims 2-8 are rejected for at least the reasons cited with respect to claim 1.
Claim 2 further mathematically limits the apparatus of claim 1. Claim 2 recites no further additional elements beyond those recited in claim 1 that would require further analysis under Step 2A prong 2 or Step 2B.
Claim 3 recites at least one SSM model that is quiescent for a period of time during the step of decoding is excluded from the subset of previously encoded SSM models during the step of matching. This limitation is being interpreted as an intended result of the math, i.e., the model being quiescent flows directly from the mathematical concepts. See specification [0011]. Furthermore the exclusion from the encoding SSM models during the step of matching comprises mathematical relationships under step 2A prong 1. Under step 2A prong 2 and step 2B the claim contains no further additional elements that would require further analysis.
Claims 4, and 8 recite no further additional elements that would require further analysis under Step 2A prong 2 and Step 2B.
Claims 5-6 under the step 2A prong 2 analysis recite the additional element of performing mathematical steps in parallel. This element is merely generally linked to a computer comprising parallel processors in a manner that merely “applies it” in a parallel processor computer and comprises an insignificant extra solution activity. Under the step 2B analysis the operation in parallel is well-understood, routine and conventional activity. See, e.g., JL Hennessy et al., Computer architecture: A quantitative approach, Elsevier Science & Technology, 2014, p. 527-548, 608-614 (hereinafter “Hennessy”) which describes numerous parallel architectures that have been described for over 30 years, and that provided for parallel, distributed and replicated operation of computing systems including application to scientific calculations including matrix calculations.
Under step 2A prong 1, claim 7 further mathematically limits the mathematical concepts recited in claim 1. Under the step 2A prong 2 analysis, claim 2 comprises the additional element of receiving a second collection of signals. This limitation comprises an insignificant extra solution activity. Under step 2B this limitation is well understood, routine and conventional activity. See Patterson Ch 1 and figure 1. 5.
Allowable Subject Matter
For the reasons set forth in the office action dated 03/14/24, claims 1-34 would be allowable if rewritten to overcome the rejections under 35 USC 101.
Conclusion
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/EMILY E LAROCQUE/Primary Examiner, Art Unit 2182