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 .
DETAILED ACTION
This action is a responsive to the application filed on 09/08/2022.
Claims 43-66 are pending.
Claims 43-66 are rejected.
Double Patenting
5. A rejection based on double patenting of the “same invention” type finds its support in the language of 35 U.S.C. 101 which states that “whoever invents or discovers any new and useful process... may obtain a patent therefor...” (Emphasis added). Thus, the term “same invention,” in this context, means an invention drawn to identical subject matter. See Miller v. Eagle Mfg. Co., 151 U.S. 186 (1894); In re Vogel, 422 F.2d 438, 164 USPQ 619 (CCPA 1970); In re Ockert, 245 F.2d 467, 114 USPQ 330 (CCPA 1957).
A statutory type (35 U.S.C. 101) double patenting rejection can be overcome by canceling or amending the claims that are directed to the same invention so they are no longer coextensive in scope. The filing of a terminal disclaimer cannot overcome a double patenting rejection based upon 35 U.S.C. 101.
Claim 54-61 and 65-66 are provisionally rejected under 35 U.S.C. 101 as claiming the same invention as that of claim 1-8 and 22-23 of copending Application No. 18/179, 990 (reference application). This is a provisional statutory double patenting rejection since the claims directed to the same invention have not in fact been patented.
Claims of Instant application (17/940,553)
Claims of co-pending application (18/179, 990)
Claim 54
Claim 1
Claim 55
Claim 2
Claim 56
Claim 3
Claim 57
Claim 4
Claim 58
Claim 5
Claim 59
Claim 6
Claim 60
Claim 7
Claim 61
Claim 8
Claim 65
Claim 22
Claim 66
Claim 23
Claims of Instant application (17/940,553)
Claims of co-pending application (18/179, 990 )
54. A method for prioritizing predictive model data streams, the method comprising:
receiving, by a device, a plurality of predictive model data streams, wherein each predictive model data stream comprises a set of model parameters for a corresponding predictive model, and wherein each predictive model is trained to predict future data values of a data source;
prioritizing, by the device, each of the plurality of predictive model data streams;
selecting at least one of the predictive model data streams based on a corresponding priority;
parameterizing, by the device, a predictive model using the set of model parameters included in the selected at least one predictive model data stream; and
predicting, by the device, the future data values of the data source using the parameterized predictive model.
55. The method of claim 54 wherein the selected at least one predictive model data stream is associated with a high priority.
56. The method of claim 54 wherein the selecting comprises suppressing the predictive model data streams that were not selected based on priorities associated with each non-selected predictive model data stream.
57. The method of claim 54 further comprising assigning priorities to each of the plurality of predictive model data streams includes determining whether each set of the model parameters is unusual.
58. The method of claim 54 further comprising assigning priorities to each of the plurality of predictive model data streams includes determining whether each set of the model parameters has changed from a previous value.
59. The method of claim 54 wherein the set of model parameters comprise at least one vector.
60. The method of claim 59 wherein the at least one vector comprises a motion vector associated with a robot.
61. The method of claim 60 wherein the future data values comprise one or more future predicted locations of the robot.
65. The method of claim 54 wherein the sensors are security cameras, wherein the data stream comprises motion vectors extracted from video data captured by the security cameras.
66. The method of claim 54 wherein the sensors are vibration sensors measuring vibrations generated by machines, wherein the future data values indicate a potential need for maintenance of the machines.
1. A method for prioritizing predictive model data streams, the method comprising:
receiving, by a device, a plurality of predictive model data streams, wherein each predictive model data stream comprises a set of model parameters for a corresponding predictive model, and wherein each predictive model is trained to predict future data values of a data source;
prioritizing, by the device, each of the plurality of predictive model data streams;
selecting at least one of the predictive model data streams based on a corresponding priority;
parameterizing, by the device, a predictive model using the set of model parameters included in the selected at least one predictive model data stream; and
predicting, by the device, the future data values of the data source using the parameterized predictive model.
2. The method of claim 1 wherein the selected at least one predictive model data stream is associated with a high priority.
3. The method of claim 1 wherein the selecting comprises suppressing the predictive model data streams that were not selected based on priorities associated with each non-selected predictive model data stream.
4. The method of claim 1 further comprising assigning priorities to each of the plurality of predictive model data streams includes determining whether each set of the model parameters is unusual.
5. The method of claim 1 further comprising assigning priorities to each of the plurality of predictive model data streams includes determining whether each set of the model parameters has changed from a previous value.
6. The method of claim 1 wherein the set of model parameters comprise at least one vector.
7. The method of claim 6 wherein the at least one vector comprises a motion vector associated with a robot.
8. The method of claim 7 wherein the future data values comprise one or more future predicted locations of the robot.
22. The system of claim 21 wherein the sensor devices are security cameras such that the predictive model data streams at least partially include motion vectors extracted from video data captured by the security cameras.
23. The system of claim 21 wherein the sensor devices are vibration sensors that measure vibrations generated by machines, and wherein the future data values indicate a potential need for maintenance of the machines based on the measured vibrations.
Claim Interpretation
The following is a quotation of 35 U.S.C. 112(f):
(f) Element in Claim for a Combination. – An element in a claim for a combination may be expressed as a means or step for performing a specified function without the recital of structure, material, or acts in support thereof, and such claim shall be construed to cover the corresponding structure, material, or acts described in the specification and equivalents thereof.
The following is a quotation of pre-AIA 35 U.S.C. 112, sixth paragraph:
An element in a claim for a combination may be expressed as a means or step for performing a specified function without the recital of structure, material, or acts in support thereof, and such claim shall be construed to cover the corresponding structure, material, or acts described in the specification and equivalents thereof.
The claims in this application are given their broadest reasonable interpretation using the plain meaning of the claim language in light of the specification as it would be understood by one of ordinary skill in the art. The broadest reasonable interpretation of a claim element (also commonly referred to as a claim limitation) is limited by the description in the specification when 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, is invoked.
As explained in MPEP § 2181, subsection I, claim limitations that meet the following three-prong test will be interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph:
(A) the claim limitation uses the term “means” or “step” or a term used as a substitute for “means” that is a generic placeholder (also called a nonce term or a non-structural term having no specific structural meaning) for performing the claimed function;
(B) the term “means” or “step” or the generic placeholder is modified by functional language, typically, but not always linked by the transition word “for” (e.g., “means for”) or another linking word or phrase, such as “configured to” or “so that”; and
(C) the term “means” or “step” or the generic placeholder is not modified by sufficient structure, material, or acts for performing the claimed function.
Use of the word “means” (or “step”) in a claim with functional language creates a rebuttable presumption that the claim limitation is to be treated in accordance with 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph. The presumption that the claim limitation is interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, is rebutted when the claim limitation recites sufficient structure, material, or acts to entirely perform the recited function.
Absence of the word “means” (or “step”) in a claim creates a rebuttable presumption that the claim limitation is not to be treated in accordance with 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph. The presumption that the claim limitation is not interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, is rebutted when the claim limitation recites function without reciting sufficient structure, material or acts to entirely perform the recited function.
Claim limitations in this application that use the word “means” (or “step”) are being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, except as otherwise indicated in an Office action. Conversely, claim limitations in this application that do not use the word “means” (or “step”) are not being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, except as otherwise indicated in an Office action.
This application includes one or more claim limitations that do not use the word “means,” but are nonetheless being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, because the claim limitation(s) uses a generic placeholder that is coupled with functional language without reciting sufficient structure to perform the recited function and the generic placeholder is not preceded by a structural modifier. Such claim limitation(s) is/are:
“First device”: receiving…generating…transmitting… in claim 43
“Second device”: receiving …parameterizing…and predicting” in claim 43
“First device: receiving…prioritizing…selecting…parameterizing…and predicting” in claim 54
Because this/these claim limitation(s) is/are being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, it/they is/are being interpreted to cover the corresponding structure described in the specification as performing the claimed function, and equivalents thereof.
If applicant does not intend to have this/these limitation(s) interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, applicant may: (1) amend the claim limitation(s) to avoid it/them being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph (e.g., by reciting sufficient structure to perform the claimed function); or (2) present a sufficient showing that the claim limitation(s) recite(s) sufficient structure to perform the claimed function so as to avoid it/them being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph.
Further, the limitations of “module[s]” for the above corresponding operations invoke 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, however, applicant’s paragraphs 1880-1900 recite sufficient structure stating the steps of generating and using a predictive model on “chips” that include “processors”.
Claim Rejections - 35 USC § 112
8. The following is a quotation of 35 U.S.C. 112(b):
(b) CONCLUSION.—The specification shall conclude with one or more claims particularly pointing out and distinctly claiming the subject matter which the inventor or a joint inventor regards as the invention.
The following is a quotation of 35 U.S.C. 112 (pre-AIA ), second paragraph:
The specification shall conclude with one or more claims particularly pointing out and distinctly claiming the subject matter which the applicant regards as his invention.
2.1 Claims 55 and 57 are rejected under 35 U.S.C. 112(b) or 35 U.S.C. 112 (pre-AIA ), second paragraph, as being indefinite for failing to particularly point out and distinctly claim the subject matter which the inventor or a joint inventor (or for applications subject to pre-AIA 35 U.S.C. 112, the applicant), regards as the invention.
The term “high” in claim 55 and “Unusual” in claim 57. is a relative term which renders the claim indefinite. The term “high” in claim 55 and “Unusual” claim 57 is not defined by the claim, the specification does not provide a standard for ascertaining the requisite degree, and one of ordinary skill in the art would not be reasonably apprised of the scope of the invention. Appropriate correction is required.
Claim Rejections - 35 USC §101
9. 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.
10. Claims 43-66 are rejected under 35 U.S.C. 101 because the claimed invention is directed to non-statutory subject matter. These claims are directed to an abstract idea without significantly more.
(Step 1) Is the claims to a process, machine, manufacture, or composition of matter?
Claims: 43-66 are directed to method or process, which falls into the one of the statutory category.
(Step 2A) (Prong 1) Is the claim directed to a law of nature, a natural phenomenon, or an abstract idea? (Judicially recognized exceptions)?
Claim 43 recites
generating, a predictive model for predicting future data values of the data stream based on the received plurality of data values, wherein generating the predictive model comprises determine a plurality of model parameters; (a modeler must form an abstract understanding (a mental model) of the system under study and decide how different variables might interact. This initial diagram or framework of key players and interactions is conceptual and guides the selection of the appropriate mathematical approach. The choice of which variables to include, the underlying assumptions about the data, and the interpretation of the model's results (e.g., what a specific parameter value reflects about a cognitive process) are all abstract, mental processes. Thus, it falls under the combination of mental process and mathematical concepts of abstract idea)
parameterizing, a predictive model using the plurality of model parameters; (mentally/with the aid of pen and paper parameterizing…a predictive model using the set of model parameters included in the selected at least one predictive model data stream (e.g. by thinking of/writing out tuning the calculation setting values to match the chosen observed thought values. Under the broadest reasonable interpretation, this limitation covers mental process including an evaluation or judgement that could be performed in the human mind or with the aid of pencil and paper therefore it falls within the “Mental Process” grouping of abstract ideas), and
predicting, the future data values of the data stream using the parameterized predictive model. (mentally/with the aid of pen and paper predicting…the future data values of the data source using the parameterized predictive model (e.g. by thinking of/writing out the tuned calculation to output values based on the input of remembered values. Under the broadest reasonable interpretation, this limitation covers mental process including an evaluation or judgement that could be performed in the human mind or with the aid of pencil and paper therefore it falls within the “Mental Process” grouping of abstract ideas).
Step 2A, Prong 2: Does the claim recite additional elements that integrate the judicial exception into a practical application?
In accordance with Step 2A, Prong 2, the judicial exception is not integrated into a practical application In particular the claim 43 recites the additional elements of receiving, by a first device, a plurality of data values of a data stream, wherein the data values comprise sensor data collected from one or more sensor devices; transmitting, by the first device, the plurality of model parameters to the second device; receiving, by the second device, the plurality of model parameters which are mere data gathering or transmission steps and thus it falls under insignificant extra-solution activity to the judicial exception, as discussed in MPEP § 2106.05(g) The additional elements of “by the first device and “by the second device” in claim 43 are merely including instructions to implement an abstract idea on a computer, or merely using a computer as a tool to perform an abstract idea, as discussed in MPEP § 2106.05(f). These additional elements do not integrate the abstract idea into a practical application because it does not impose any meaningful limits on practicing the abstract idea.
Step 2B: Does the claim recite additional elements that amount to significantly more than the judicial exception?
In accordance with Step 2B, the claims do not include additional elements that are sufficient to amount to significantly more than the judicial exception. In accordance with Step 2A, Prong 2, the judicial exception is not integrated into a practical application. In particular the claim 43 recites the additional elements of receiving, by a first device, a plurality of data values of a data stream, wherein the data values comprise sensor data collected from one or more sensor devices; transmitting, by the first device, the plurality of model parameters to the second device; receiving, by the second device, the plurality of model parameters which are mere data gathering or transmission steps and thus it falls under insignificant extra-solution activity to the judicial exception, as discussed in MPEP § 2106.05(g) and is well-understood, routine or conventional. ((See MPEP 2106.05 (d)(II)(i))) Receiving or transmitting data over a network, e.g., using the Internet to gather data, Symantec, 838 F.3d at 1321, 120 USPQ2d at 1362 (utilizing an intermediary computer to forward information); TLI Communications LLC v. AV Auto. LLC, 823 F.3d 607, 610, 118 USPQ2d 1744, 1745 (Fed. Cir. 2016) (using a telephone for image transmission); OIP Techs., Inc., v.Amazon.com, Inc., 788 F.3d 1359, 1363, 115 USPQ2d 1090, 1093 (Fed. Cir. 2015) (sending messages over a network); buySAFE, Inc. v. Google, Inc., 765 F.3d 1350, 1355, 112 USPQ2d 1093, 1096 (Fed. Cir. 2014) (computer receives and sends information over a network); but see DDR Holdings, LLC v. Hotels.com, L.P., 773 F.3d 1245, 1258, 113 USPQ2d 1097, 1106 (Fed. Cir. 2014). The additional elements of “by the first device and “by the second device” in claim 43 are merely including instructions to implement an abstract idea on a computer, or merely using a computer as a tool to perform an abstract idea, as discussed in MPEP § 2106.05(f). The claim does not include any additional element; thus, it does not integrate the judicial exception into a practical application nor amount to significantly more than the judicial exception. Thus, claim 43 is not patent eligible.
Regarding claim 54
(Step 2A) (Prong 1) Is the claim directed to a law of nature, a natural phenomenon, or an abstract idea? (Judicially recognized exceptions)?
Claim 43 recites
A method for prioritizing predictive model data streams, the method comprising:
wherein each predictive model is trained to predict future data values of a data source; (mentally/with the aid of pen and paper receiving…a plurality of predictive model data streams, wherein each predictive model data stream comprises a set of model parameters for a corresponding predictive model, and wherein each predictive model is trained to predict future data values of a data source (e.g. by thinking of/writing out multiple observed thoughts associated with a written calculation setting values that tune the model to output values based on the input of remembered values. Under the broadest reasonable interpretation, this limitation covers mental process including an evaluation or judgement that could be performed in the human mind or with the aid of pencil and paper therefore it falls within the “Mental Process” grouping of abstract idea),
prioritizing, priorities to each of the plurality of predictive model data streams; (mentally/with the aid of pen and paper prioritizing…each of the plurality of predictive model data streams (e.g. by thinking of/writing out ranking the multiple observed thoughts), Under the broadest reasonable interpretation, this limitation covers mental process including an evaluation or judgement that could be performed in the human mind or with the aid of pencil and paper therefore it falls within the “Mental Process” grouping of abstract idea)
selecting at least one of the predictive model data streams based on a corresponding priority; (mentally/with the aid of pen and paper selecting at least one of the predictive model data streams based on a corresponding priority (e.g. by thinking of/writing out choosing the number one ranked observed thought. Under the broadest reasonable interpretation, this limitation covers mental process including an evaluation or judgement that could be performed in the human mind or with the aid of pencil and paper therefore it falls within the “Mental Process” grouping of abstract ideas))
parameterizing, a predictive model using the set of model parameters included in the selected predictive model stream; (mentally/with the aid of pen and paper parameterizing…a predictive model using the set of model parameters included in the selected at least one predictive model data stream (e.g. by thinking of/writing out tuning the calculation setting values to match the chosen observed thought values. Under the broadest reasonable interpretation, this limitation covers mental process including an evaluation or judgement that could be performed in the human mind or with the aid of pencil and paper therefore it falls within the “Mental Process” grouping of abstract idea))and
predicting, future data values of the data source using the parameterized predictive model. (mentally/with the aid of pen and paper predicting…the future data values of the data source using the parameterized predictive model (e.g. by thinking of/writing out the tuned calculation to output values based on the input of remembered values). Under the broadest reasonable interpretation, this limitation covers mental process including an evaluation or judgement that could be performed in the human mind or with the aid of pencil and paper therefore it falls within the “Mental Process” grouping of abstract idea)
Step 2A, Prong 2: Does the claim recite additional elements that integrate the judicial exception into a practical application?
In accordance with Step 2A, Prong 2, the judicial exception is not integrated into a practical application In particular the claim 54 recites the additional elements of receiving, by a first device, a plurality of predictive model data streams, wherein each predictive model data streams comprises a set of model parameters for a corresponding predictive model which are mere data gathering steps and thus it falls under insignificant extra-solution activity to the judicial exception, as discussed in MPEP § 2106.05(g) The additional elements of “by the first device and “by the second device” in claim 54 are merely including instructions to implement an abstract idea on a computer, or merely using a computer as a tool to perform an abstract idea, as discussed in MPEP § 2106.05(f). These additional elements do not integrate the abstract idea into a practical application because it does not impose any meaningful limits on practicing the abstract idea.
Step 2B: Does the claim recite additional elements that amount to significantly more than the judicial exception?
In accordance with Step 2B, the claims do not include additional elements that are sufficient to amount to significantly more than the judicial exception. In accordance with Step 2A, Prong 2, the judicial exception is not integrated into a practical application. In particular the claim 54 recites the additional elements of receiving, by a first device, a plurality of predictive model data streams, wherein each predictive model data streams comprises a set of model parameters for a corresponding predictive model which are mere data gathering steps and thus it falls under insignificant extra-solution activity to the judicial exception, as discussed in MPEP § 2106.05(g) and is well-understood, routine or conventional. ((See MPEP 2106.05 (d)(II)(i))) Receiving or transmitting data over a network, e.g., using the Internet to gather data, Symantec, 838 F.3d at 1321, 120 USPQ2d at 1362 (utilizing an intermediary computer to forward information); TLI Communications LLC v. AV Auto. LLC, 823 F.3d 607, 610, 118 USPQ2d 1744, 1745 (Fed. Cir. 2016) (using a telephone for image transmission); OIP Techs., Inc., v.Amazon.com, Inc., 788 F.3d 1359, 1363, 115 USPQ2d 1090, 1093 (Fed. Cir. 2015) (sending messages over a network); buySAFE, Inc. v. Google, Inc., 765 F.3d 1350, 1355, 112 USPQ2d 1093, 1096 (Fed. Cir. 2014) (computer receives and sends information over a network); but see DDR Holdings, LLC v. Hotels.com, L.P., 773 F.3d 1245, 1258, 113 USPQ2d 1097, 1106 (Fed. Cir. 2014). The additional elements of “by the first device” in claim 54 are merely including instructions to implement an abstract idea on a computer, or merely using a computer as a tool to perform an abstract idea, as discussed in MPEP § 2106.05(f). The claim does not include any additional element; thus, it does not integrate the judicial exception into a practical application nor amount to significantly more than the judicial exception. Thus, claim 54 is not patent eligible.
Claim 55 further recites wherein the selected at least one predictive model data stream is associated with a high priority. A person mentally/with the aid of pen and paper wherein the selected at least one predictive model data stream is associated with a high priority (e.g. by mentally/writing out choosing the number one ranked observed thought). Under the broadest reasonable interpretation, this limitation covers mental process including an evaluation or judgement that could be performed in the human mind or with the aid of pencil and paper therefore it falls within the “Mental Process” grouping of abstract idea. The claim does not include any additional element; thus, it does not integrate the judicial exception into a practical application nor amount to significantly more than the judicial exception.
Claim 56 further recites wherein the selecting comprises suppressing the predictive model data streams that were not selected based on the priorities associated with each non-selected predictive model data stream. A person mentally/with the aid of pen and paper wherein the selecting comprises suppressing the predictive model data streams that were not selected based on priorities associated with each non-selected predictive model data stream (e.g. by mentally/writing out not choosing the ranked observed thoughts that were not number one. Under the broadest reasonable interpretation, this limitation covers mental process including an evaluation or judgement that could be performed in the human mind or with the aid of pencil and paper therefore it falls within the “Mental Process” grouping of abstract idea. The claim does not include any additional element; thus, it does not integrate the judicial exception into a practical application nor amount to significantly more than the judicial exception.
Claim 57 further recites wherein assigning priorities to each of the plurality of predictive model data streams comprises determining whether each set of model parameters is unusual. A person mentally/with the aid of pen and paper further comprising assigning priorities to each of the plurality of predictive model data streams includes determining whether each set of the model parameters is unusual (e.g. by mentally/writing out ranking the multiple observed thoughts based on outlier value presence). Under the broadest reasonable interpretation, this limitation covers mental process including an evaluation or judgement that could be performed in the human mind or with the aid of pencil and paper therefore it falls within the “Mental Process” grouping of abstract idea. The claim does not include any additional element; thus, it does not integrate the judicial exception into a practical application nor amount to significantly more than the judicial exception.
Claim 58 further recites wherein assigning priorities to each of the plurality of predictive model data streams comprises determining whether each set of model parameters has changed from a previous value. A person mentally/with the aid of pen and paper further comprising assigning priorities to each of the plurality of predictive model data streams includes determining whether each set of the model parameters has changed from a previous value (e.g. by mentally/writing out ranking the multiple observed thoughts based on which values have been updated since last observed). Under the broadest reasonable interpretation, this limitation covers mental process including an evaluation or judgement that could be performed in the human mind or with the aid of pencil and paper therefore it falls within the “Mental Process” grouping of abstract idea. The claim does not include any additional element; thus, it does not integrate the judicial exception into a practical application nor amount to significantly more than the judicial exception.
Claim 44 and 59 further recites wherein the set of model parameters comprise at least one vector. A person mentally/with the aid of pen and paper wherein the set of model parameters comprise at least one vector (e.g. by mentally/writing out the written calculation setting values include vectorized data) Under the broadest reasonable interpretation, this limitation covers mental process including an evaluation or judgement that could be performed in the human mind or with the aid of pencil and paper therefore it falls within the “Mental Process” grouping of abstract idea. The claim does not include any additional element; thus, it does not integrate the judicial exception into a practical application nor amount to significantly more than the judicial exception.
Claim 45 and 60 further recites wherein the at least one vector comprises a motion vector associated with a robot. A person mentally/with the aid of pen and paper wherein the at least one vector comprises a motion vector associated with a robot (e.g. by mentally/writing out the vectorized data includes a movement vector of a remembered robot)Under the broadest reasonable interpretation, this limitation covers mental process including an evaluation or judgement that could be performed in the human mind or with the aid of pencil and paper therefore it falls within the “Mental Process” grouping of abstract idea. The claim does not include any additional element; thus, it does not integrate the judicial exception into a practical application nor amount to significantly more than the judicial exception.
Claim 46 and 61 further recites wherein the future data values comprise one or more future predicted locations of the robot. A person mentally/with the aid of pen and paper wherein the future data values comprise one or more future predicted locations of the robot (e.g. by mentally/writing out the calculation output includes a predicted placement of the remembered robot). Under the broadest reasonable interpretation, this limitation covers mental process including an evaluation or judgement that could be performed in the human mind or with the aid of pencil and paper therefore it falls within the “Mental Process” grouping of abstract idea. The claim does not include any additional element; thus, it does not integrate the judicial exception into a practical application nor amount to significantly more than the judicial exception.
Claim 47 and 62 further recites wherein the predictive model is a behavior analysis model, wherein the future data values indicate a predicted behavior of an entity. A person mentally/with the aid of pen and paper wherein the predictive model is a behavior analysis model (e.g. by mentally/writing out the calculation in a behavior model architecture). Under the broadest reasonable interpretation, this limitation covers mental process including an evaluation or judgement that could be performed in the human mind or with the aid of pencil and paper therefore it falls within the “Mental Process” grouping of abstract idea. The claim does not include any additional element; thus, it does not integrate the judicial exception into a practical application nor amount to significantly more than the judicial exception.
Claim 48 and 63 further recites wherein the predictive model is an augmentation model, wherein the future data values correspond to an inoperative sensor. A person mentally/with the aid of pen and paper wherein the predictive model is a augmentation model (e.g. by mentally/writing out the calculation in a future data values of an inoperative sensor). Under the broadest reasonable interpretation, this limitation covers mental process including an evaluation or judgement that could be performed in the human mind or with the aid of pencil and paper therefore it falls within the “Mental Process” grouping of abstract idea. The claim does not include any additional element; thus, it does not integrate the judicial exception into a practical application nor amount to significantly more than the judicial exception.
Claim 49 and 64 further recites wherein the predictive model is a classification model, wherein the future data values indicate a predicted future state of a system comprising the one or more sensor devices. A person •mentally/with the aid of pen and paper wherein the predictive model is a classification model (e.g. by mentally/writing out the calculation in a classification model architecture). Under the broadest reasonable interpretation, this limitation covers mental process including an evaluation or judgement that could be performed in the human mind or with the aid of pencil and paper therefore it falls within the “Mental Process” grouping of abstract idea. The claim does not include any additional element; thus, it does not integrate the judicial exception into a practical application nor amount to significantly more than the judicial exception.
Claim 50 and 65 further recites wherein the sensors are security cameras, wherein the data stream comprises motion vectors extracted from video data captured by the security cameras. It is recited at a high level generally link the use of the judicial exception to a particular technological environment or field of use, and do not integrate the abstract idea into a practical application because they do not impose any meaningful limits on practicing the abstract idea (Step 2A, Prong 2; see MPEP 2106.05(h)). The additional elements in the claims do not amount to significantly more than an abstract idea. As discussed above with respect to the integration of the abstract idea into a practical application, the additional elements to perform the steps of in the dependent claims and perform the steps of the claims amount to no more than mere instructions to apply the exception using generic computer components and generally link the use of the judicial exception to a particular technological environment or field of use. Generally linking the use of the judicial exception to a particular technological environment or field of use cannot provide an inventive concept. (STEP 2B). As such, dependent claims 50 and 65 additional elements or combination of elements do not amount to significantly more than an abstract idea nor provide any inventive concept, nor impose a meaningful limit to integrate the elements into a practical application or significantly more than the judicial exceptions; therefore, the dependent claims are not deemed patent eligible.
Claim 51 and 66 further recites wherein the sensors are vibration sensors measuring vibrations generated by machines, wherein the future data values indicate a potential need for maintenance of the machines. It is recited at a high level generally link the use of the judicial exception to a particular technological environment or field of use, and do not integrate the abstract idea into a practical application because they do not impose any meaningful limits on practicing the abstract idea (Step 2A, Prong 2; see MPEP 2106.05(h)). The additional elements in the claims do not amount to significantly more than an abstract idea. As discussed above with respect to the integration of the abstract idea into a practical application, the additional elements to perform the steps of in the dependent claims and perform the steps of the claims amount to no more than mere instructions to apply the exception using generic computer components and generally link the use of the judicial exception to a particular technological environment or field of use. Generally linking the use of the judicial exception to a particular technological environment or field of use cannot provide an inventive concept. (STEP 2B). As such, dependent claims 51 and 66 additional elements or combination of elements do not amount to significantly more than an abstract idea nor provide any inventive concept, nor impose a meaningful limit to integrate the elements into a practical application or significantly more than the judicial exceptions; therefore, the dependent claims are not deemed patent eligible.
Claim 52 further recites refining, by the first device, the predictive model using the additional data values, wherein refining the predictive model adjusts the model parameters; Under the broadest reasonable interpretation, this limitation covers mental process including an evaluation or judgement that could be performed in the human mind or with the aid of pencil and paper therefore it falls within the “Mental Process” grouping of abstract idea. The additional elements of receiving, by the first device, additional data values of the data stream and transmitting the adjusted model parameters to the second device which are mere data gathering or transmission steps and thus it falls under insignificant extra-solution activity to the judicial exception, as discussed in MPEP § 2106.05(g) and is well-understood, routine or conventional. ((See MPEP 2106.05 (d)(II)(i))) Receiving or transmitting data over a network, e.g., using the Internet to gather data, Symantec, 838 F.3d at 1321, 120 USPQ2d at 1362 (utilizing an intermediary computer to forward information); TLI Communications LLC v. AV Auto. LLC, 823 F.3d 607, 610, 118 USPQ2d 1744, 1745 (Fed. Cir. 2016) (using a telephone for image transmission); OIP Techs., Inc., v.Amazon.com, Inc., 788 F.3d 1359, 1363, 115 USPQ2d 1090, 1093 (Fed. Cir. 2015) (sending messages over a network); buySAFE, Inc. v. Google, Inc., 765 F.3d 1350, 1355, 112 USPQ2d 1093, 1096 (Fed. Cir. 2014) (computer receives and sends information over a network); but see DDR Holdings, LLC v. Hotels.com, L.P., 773 F.3d 1245, 1258, 113 USPQ2d 1097, 1106 (Fed. Cir. 2014). The claim does not include any additional element; thus, it does not integrate the judicial exception into a practical application nor amount to significantly more than the judicial exception.
Claim 53 further recites re-parameterizing the predictive model using the adjusted model parameters; and
generating additional future data values using the re-parameterized predictive model. Under the broadest reasonable interpretation, this limitation covers mental process including an evaluation or judgement that could be performed in the human mind or with the aid of pencil and paper therefore it falls within the “Mental Process” grouping of abstract idea. The additional elements of receiving, by the second device, the adjusted model parameters which are mere data gathering steps and thus it falls under insignificant extra-solution activity to the judicial exception, as discussed in MPEP § 2106.05(g) and is well-understood, routine or conventional. ((See MPEP 2106.05 (d)(II)(i))) Receiving or transmitting data over a network, e.g., using the Internet to gather data, Symantec, 838 F.3d at 1321, 120 USPQ2d at 1362 (utilizing an intermediary computer to forward information); TLI Communications LLC v. AV Auto. LLC, 823 F.3d 607, 610, 118 USPQ2d 1744, 1745 (Fed. Cir. 2016) (using a telephone for image transmission); OIP Techs., Inc., v.Amazon.com, Inc., 788 F.3d 1359, 1363, 115 USPQ2d 1090, 1093 (Fed. Cir. 2015) (sending messages over a network); buySAFE, Inc. v. Google, Inc., 765 F.3d 1350, 1355, 112 USPQ2d 1093, 1096 (Fed. Cir. 2014) (computer receives and sends information over a network); but see DDR Holdings, LLC v. Hotels.com, L.P., 773 F.3d 1245, 1258, 113 USPQ2d 1097, 1106 (Fed. Cir. 2014). The claim does not include any additional element; thus, it does not integrate the judicial exception into a practical application nor amount to significantly more than the judicial exception.
Claim Rejections - 35 USC § 102
11. 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 the appropriate paragraphs of 35 U.S.C. 102 that form the basis for the rejections under this section made in this Office action:
A person shall be entitled to a patent unless –
(a)(2) the claimed invention was described in a patent issued under section 151, or in an application for patent published or deemed published under section 122(b), in which the patent or application, as the case may be, names another inventor and was effectively filed before the effective filing date of the claimed invention.
12. Claim(s) 43, 47, 49, 51-55, 62, 64 and 66 is/are rejected under 35 U.S.C. 102 (a)(2) as being anticipated by Kaira et al. (PUB NO: US20220004174A1)
Regarding claim 43
Kaira teaches a method for transmitting a predictive model of a data stream from a first device to a second device, (see para 51- Moreover, the data streams generated by the controllers 110 a-d can be ingested and analyzed—at the edge or in the cloud—to train predictive analytics models using machine learning algorithms. see para 161 and fig 9- Given the variety of types of applicable communications from the device to another component or network, applicable communications circuitry used by the device may include or be embodied by any one or more of components 964, 966, 968, or 970. Accordingly, in various examples, applicable means for communicating (e.g., receiving, transmitting, etc.) may be embodied by such communications circuitry. the method comprising:
receiving, by a first device, a plurality of data values of a data stream, wherein the data values comprise sensor data collected from one or more sensor devices;(see para 95- In some embodiments, for example, computing device 400 may receive data streams generated by tools 416 and/or robots 418 (and/or their associated sensors 414). See fig 5 and para 107-108-For example, the data stream may include a set of feature values corresponding to an unlabeled instance of a feature set.)
generating, by the first device, a predictive model for predicting future data values of the data stream based on the received plurality of data values, (see para 77 and fig 2-At the model development phase, the grouping function 208 is applied to the existing training dataset 202. For example, the grouping function is used to split the existing training dataset 202 into smaller training datasets or groups based on machine characteristics. The resulting training datasets are then used to train and create machine learning models 210 a-n (e.g., predictive models), each of which applies to a specific machine group. For example, each model 210 a-n is trained to predict a target variable based on the training dataset 202 for a particular group. The target variable can include any type of predicted information depending on the particular use case that the models 210 a-n are developed and trained for (e.g., a predicted quality level for a quality control use case).
wherein generating the predictive model comprises determine a plurality of model parameters; (see para 64-(i) A machine learning model is used to determine parameters from the training dataset 202 (e.g., a set of labeled data points or data streams) that characterize machine groupings. The machine learning model may be implemented using any suitable data grouping model or clustering model, such as k-means clustering.)
transmitting, by the first device, the plurality of model parameters to the second device; receiving, by the second device, the plurality of model parameters; (see para 148- The communication circuitry 912 may be embodied as any communication circuit, device, or collection thereof, capable of enabling communications over a network between the compute circuitry 902 and another compute device (e.g., an edge gateway of an implementing edge computing system). see para 166-A battery monitor/charger 978 may be included in the edge computing node 950 to track the state of charge (SoCh) of the battery 976, if included. The battery monitor/charger 978 may be used to monitor other parameters of the battery 976 to provide failure predictions, such as the state of health (SoH) and the state of function (SoF) of the battery 976. The battery monitor/charger 978 may communicate the information on the battery 976 to the processor 952 over the interconnect 956. The battery monitor/charger 978 may also include an analog-to-digital (ADC) converter that enables the processor 952 to directly monitor the voltage of the battery 976 or the current flow from the battery 976. The battery parameters may be used to determine actions that the edge computing node 950 may perform, such as transmission frequency, mesh network operation, sensing frequency, and the like.)
parameterizing, by the second device, a predictive model using the plurality of model parameters; (see para 55-60- In some embodiments, for example, the described solution may include the following features and functionality:(i) analyzing the data streams to characterize similarities of their underlying data;(ii) creating groups of streams based on the similarities; (iii) creating models per group and validating convergence of machine learning models; (iv) dynamically reconfiguring group(s) based on change in data stream characteristics with appropriate model tuning; and/or (v) tracking telemetry of adaptive reconfigurations to feed forward learnings for future model development and predictive maintenance of the autonomous agents.)
Examiner note: The configuration of multiple distinct models, where each model is defined by its specific parameters. Model convergence is the process where a model's loss settles within an error range around a final value during training. This process directly involves adjusting the model's internal parameters through optimization algorithms to reach an optimal or stable state. Model tuning" is synonymous with adjusting or optimizing a model's parameters (or hyperparameters) to improve performance or adapt to new data characteristics.
and
predicting, by the second device, the future data values of the data stream using the parameterized predictive model. (see para 77 and fig 2-At the model development phase, the grouping function 208 is applied to the existing training dataset 202. For example, the grouping function is used to split the existing training dataset 202 into smaller training datasets or groups based on machine characteristics. The resulting training datasets are then used to train and create machine learning models 210 a-n (e.g., predictive models), each of which applies to a specific machine group. For example, each model 210 a-n is trained to predict a target variable based on the training dataset 202 for a particular group. The target variable can include any type of predicted information depending on the particular use case that the models 210 a-n are developed and trained for (e.g., a predicted quality level for a quality control use case).
Regarding claim 54
Kaira teaches a method for prioritizing predictive model data streams, (see para 130 and fig 2- The various use cases 705 may access resources under usage pressure from incoming streams, due to multiple services utilizing the edge cloud. To achieve results with low latency, the services executed within the edge cloud 610 balance varying requirements in terms of: (a) Priority (throughput or latency) and Quality of Service (QoS) (e.g., traffic for an autonomous car may have higher priority than a temperature sensor in terms of response time requirement; or, a performance sensitivity/bottleneck may exist at a compute/accelerator, memory, storage, or network