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
1. The following is a non-final, First Office Action on the merits. Claims 2-21 are pending. Claim 1 has been canceled. Claims 2-21 are new.
The Examiner’s note:
2. Independent claims 2, 9 and 16 as a whole recites a combination of limitations that are defined over prior art of record {the combination of Paff et al; (US 2018/0365295 A1), Duke; (US 2019/0392487 A1), Okamoto; (US 2020/0293898 A1), Vo; (US 2021/0334698 A1); and Zhang; (US 2020/0349441 A1)} teaches elements of the claimed invention. However, it would be hind-sight reasoning to combine the individual elements disclosed in the prior-art in order to achieve Applicant's claimed invention.
Please notes of the double patenting rejection and 101’s rejection below.
Double Patenting
3. Independent Claims 2, 9 and 16 are rejected on the ground of nonstatutory double patenting as being unpatentable respectively over claims 1 and 11 of Patent US 12,306,898 B2. Further, Independent Claims 2, 9 and 16 are rejected on the ground of nonstatutory double patenting as being unpatentable respectively over claims 1 and 13 of Patent US 11,790,030 B2. See the below:
Instant Application: case 19/205,503
Patent 12,306,898 B2
Patent US 11,790,030 B2
Claims 2, 9 and 16: A computer-implemented method comprising:
generating, by a configuration interpreter, a configuration file comprising a plurality of content attributes and corresponding probability distributions for values of the plurality of content attributes;
determining, by an analysis system, a causal relation between the plurality of content attributes and user response data; fitting, by a function fitter comprising a trained neural network, a multidimensional function to at least a part of the plurality of content attributes and an objective function extracted from the user response data; and
generating, utilizing the function fitter to reduce a dimensionality of the configuration file based on the causal relation between the plurality of content attributes and the user response data, a modified configuration file comprising updated content attributes and updated probability distributions according to the multidimensional function.
Claims 1 and 11: A method for the selection and presentation of content to a user device, the method comprising:
creating, by a configuration generator, a configuration file comprising a plurality of content attributes with a distribution indicating a probability of occurrence of a value of an attribute and a plurality of context variables;
storing, in a version control system, the configuration file;
determining, by an attribute solver, a plurality of attribute values corresponding to the plurality of attributes, wherein the determining is based on an interaction of the plurality of attributes with the plurality of context variables, and wherein the plurality of context variables are not controlled by the attribute solver;
producing, by a production system, content based on the plurality of attribute values, wherein the content is procedurally generated; displaying, on the user device, the content;
obtaining, from the user device, a user response data corresponding to a response of the user with the presented content;
fitting a multidimensional function, via a trained neural network executing on the attribute solver, to at least a part of the plurality of attributes, the plurality of context variables, and one or more objective functions extracted from the user response data; determining a causal relation between a plurality of variables comprising the plurality of attributes, the plurality of context variables, and the user response data;
upon determining the causal relation, increasing speed of calculating the multidimensional function by removing a dimension associated with the plurality of variables having the causal relation with each other; and modifying the configuration file using a plurality of next attribute values and a plurality of next ranges associated with the plurality of next attribute values determined based on the multidimensional function.
Claims 1 and 13: A method to continually generate an effective advertisement by determining attributes of an advertisement presented to a user at a user device, the method comprising:
training a neural network to fit attribute values and context variables to a set of user response data;
obtaining a plurality of attributes of the advertisement to be presented at the user device and a plurality of context variables, wherein the plurality of attributes comprises a percentage of a screen of the user device occupied by advertisements, and advertisement quality control, wherein an attribute in the plurality of attributes comprises a continuous value, wherein a range of acceptable values is uncertain, and wherein the plurality of context variables comprises at least a device type of the user device;
creating a configuration file having each of the plurality of attributes with a distribution indicating a probability of occurrence of a value of an attribute, wherein the configuration file is adapted to be stored in a version control system;
generating a plurality of attribute values corresponding to the plurality of attributes based on the configuration file;
generating the advertisement based on the plurality of attribute values;
presenting the generated advertisement to a device associated with the user;
obtaining a user response data to the presented advertisement;
fitting a multidimensional function to at least a part of the plurality of attributes, the plurality of context variables, and one or more objective functions extracted from the user response data by applying the neural network to the plurality of attributes, the plurality of context variables, and the one or more objective functions; determining a causal relation between a plurality of variables comprising the plurality of attributes, the plurality of context variables, and the user response data by analyzing the plurality of values, the plurality of attributes, and the user response data;
upon determining the causal relation, increasing speed of calculating the multidimensional function by removing a dimension associated with the plurality of variables having the causal relation with each other; and modifying the configuration file using a plurality of next attribute values and a plurality of next ranges associated with the plurality of next attribute values determined based on the multidimensional function, wherein the plurality of next attribute values and the plurality of next ranges indicate an improvement in the user response data.
In summary, independent claims 1 and 11 of Patent US 12,306,898 B2 teach all the features of independent claims 2, 9 and 16 of the instant application as indicated in the table above. Although claims 1 and 11 of Patent US 12,306,898 B2 has additional underlined features (bold limitations in the table above) such as {e.g., storing, in a version control system, the configuration file; determining, by an attribute solver, a plurality of attribute values corresponding to the plurality of attributes, wherein the determining is based on an interaction of the plurality of attributes with the plurality of context variables, and wherein the plurality of context variables are not controlled by the attribute solver; producing, by a production system, content based on the plurality of attribute values, wherein the content is procedurally generated; displaying, on the user device, the content; obtaining, from the user device, a user response data corresponding to a response of the user with the presented content}; however, it has been held that deleting elements when the function of element is not desired is obvious. See MPEP 2144.04 Section II. Therefore, it would have been obvious to a person of ordinary skill in the art at the time the invention was made to modify or to omit the additional elements of claims 1 and 11 of Patent US 12,306,898 B2 the to arrive at the claims 2, 9 and 16 of the instant application because the person of ordinary skill would have realized that the remaining element would perform the same functions as before. “Omission of element and its function in combination is obvious expedient if the remaining elements perform same functions as before.” See In re Karlson (CCPA) 136 USPQ 184, decide Jan 16, 1963, Appl. No. 6857, U. S. Court of Customs and Patent Appeals.
Further, independent claims 1 and 13 of U.S. Patent 11,790,030 B2 teach all the features of independent claims 2, 9 and 16 of the instant application as indicated in the table above. Although claims 1 and 13 of U.S. Patent 11,790,030 B2 has additional underlined features (bold limitations in the table above) such as {e.g., training a neural network to fit attribute values and context variables to a set of user response data; obtaining a plurality of attributes of the advertisement to be presented at the user device and a plurality of context variables, wherein the plurality of attributes comprises a percentage of a screen of the user device occupied by advertisements, and advertisement quality control, wherein an attribute in the plurality of attributes comprises a continuous value, wherein a range of acceptable values is uncertain, and wherein the plurality of context variables comprises at least a device type of the user device; creating a configuration file having each of the plurality of attributes with a distribution indicating a probability of occurrence of a value of an attribute, wherein the configuration file is adapted to be stored in a version control system; generating a plurality of attribute values corresponding to the plurality of attributes based on the configuration file; generating the advertisement based on the plurality of attribute values; presenting the generated advertisement to a device associated with the user; obtaining a user response data to the presented advertisement; determining a causal relation between a plurality of variables comprising the plurality of attributes, the plurality of context variables, and the user response data by analyzing the plurality of values, the plurality of attributes, and the user response data; and wherein the plurality of next attribute values and the plurality of next ranges indicate an improvement in the user response data}; however, it has been held that deleting elements when the function of element is not desired is obvious. See MPEP 2144.04 Section II. Therefore, it would have been obvious to a person of ordinary skill in the art at the time the invention was made to modify or to omit the additional elements of 1 and 13 of U.S. Patent 11,790,030 B2 the to arrive at the claims 2, 9 and 16 of the instant application because the person of ordinary skill would have realized that the remaining element would perform the same functions as before. “Omission of element and its function in combination is obvious expedient if the remaining elements perform same functions as before.” See In re Karlson (CCPA) 136 USPQ 184, decide Jan 16, 1963, Appl. No. 6857, U. S. Court of Customs and Patent Appeals.
The nonstatutory double patenting rejection is based on a judicially created doctrine grounded in public policy (a policy reflected in the statute) so as to prevent the unjustified or improper timewise extension of the “right to exclude” granted by a patent and to prevent possible harassment by multiple assignees. A nonstatutory obviousness-type double patenting rejection is appropriate where the conflicting claims are not identical, but at least one examined application claim is not patentably distinct from the reference claim(s) because the examined application claim is either anticipated by, or would have been obvious over, the reference claim(s). See, e.g., In re Berg, 140 F.3d 1428, 46 USPQ2d 1226 (Fed. Cir. 1998); In re Goodman, 11 F.3d 1046, 29 USPQ2d 2010 (Fed. Cir. 1993); In re Longi, 759 F.2d 887, 225 USPQ 645 (Fed. Cir. 1985); In re Van Ornum, 686 F.2d 937, 214 USPQ 761 (CCPA 1982); In re Vogel, 422 F.2d 438, 164 USPQ 619 (CCPA 1970); and In re Thorington, 418 F.2d 528, 163 USPQ 644 (CCPA 1969).
A timely filed terminal disclaimer in compliance with 37 CFR 1.321(c) or 1.321(d) may be used to overcome an actual or provisional rejection based on a nonstatutory double patenting ground provided the conflicting application or patent either is shown to be commonly owned with this application, or claims an invention made as a result of activities undertaken within the scope of a joint research agreement.
Effective January 1, 1994, a registered attorney or agent of record may sign a terminal disclaimer. A terminal disclaimer signed by the assignee must fully comply with 37 CFR 3.73(b).
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.
4. The claimed invention (Claims 2-21) is directed to a judicial exception (i.e., a law of nature, a natural phenomenon, or an abstract idea) without significantly more. The claim(s) recite(s) abstract ideas including “Certain Methods of Organizing Human Activity”, and/or “Mental Processes”, and/or Mathematical Concepts, which has/have been identified/found by the courts as abstract ideas in MPEP 2106.04(a). This judicial exception is not integrated into a practical application because it does not impose any meaningful limits on practicing the abstract idea. The claim(s) does/do not include additional elements that are sufficient to amount to significantly more than the judicial exception because It/they is/are recited at a high level of generality and/or are recited as performing generic computer functions routinely used in the computer applications:
5. Step 1: Does the Claim Fall within a statutory Category?
Claims 2-8: Yes, these claims are method, and therefore are directed to the statutory class of process.
Claims 9-15: Yes. These claims are systems which recites one or more processors; a memory couple to then one or more processors….. and therefore, are directed to the statutory class of machine and article of manufacture.
Claims 16-21: Yes. These claims recite a non-transitory computer-readable medium, which is interpreted as a system because they recite when executed by at least one processor…., and therefore are directed to the statutory class of machine.
6. Step 2A prong 1, Step 2A prong 2 and Step 2B:
Independent claim 12 (Step 2A, Prong I): is directed to an abstract idea of “Certain Methods of Organizing Human Activity”, and/or “Mental Processes”, and/or “Mathematical Concepts”:
Claim 9, limitations 1-4 of generating, a file comprising a plurality of content attributes and corresponding probability distributions for values of the plurality of content attributes (limitation 1); determining a causal relation between the plurality of content attributes and user response data (limitation 2); fitting, a multidimensional function to at least a part of the plurality of content attributes and an objective function extracted from the user response data (limitation 3); and generating, to reduce a dimensionality of the file based on the causal relation between the plurality of content attributes and the user response data, a modified file comprising updated content attributes and updated probability distributions according to the multidimensional function (limitation 4) fall within “Certain Methods of Organizing Human Activity” grouping of abstract idea because these steps mainly describe the concepts of commercial or legal interactions (including agreements in the form of contracts; legal obligations; advertising, marketing or sales activities or behaviors; business relations) and/or managing personal behavior or relationships or interactions between people (including following rules or instructions).
Also, the limitation 2 mentioned above of “determining a causal relation between the plurality of content attributes and user response data” also falls within the abstract “Mental Processes” grouping of abstract ideas since this limitation covers performance of the limitation in the mind. For example, a human being can observing/evaluating/analyzing the plurality of content attributes and user response data in order to determine a causal relation between the plurality of content attributes and user response data.
Further, the limitations 1-4 mentioned above also falls within the abstract “Mathematical concepts” (mathematical relationships, mathematical calculations) grouping of abstract ideas. Generating a plurality of attribute values and corresponding probability distributions for values of the plurality of content attributes…; determining a causal relation between a plurality of content attributes…; the claimed prediction function (a multidimensional function); updated content attributes, and updated probability distributions according to the multidimensional function clearly encompass a wide range of mathematical concepts such as mathematical relationships, mathematical formulas or equations, mathematical calculations.
Independent claim 9, Step 2A (Prong II): Accordingly, the claim recites an abstract idea(s) as pointed out above. This judicial exception(s) is/are not integrated into a practical application. In particular, the claim recites additional underlined elements (e.g., one or more processors; a memory coupled to the one or more processors, the memory include instructions executable by the one or more processors…; a configuration interpreter; an analysis system; a configuration file, a function filter/the function filter comprising a trained neural network…. ) to perform abstract steps/limitations 1-4 mentioned above. The additional element(s) in all of the steps is/are -recited at a high-level of generality such that it amounts no more than mere instructions to apply the judicial exception(s) using a generic computer component (e.g., one or more processors; a memory coupled to the one or more processors, the memory include instructions executable by the one or more processors…; a configuration interpreter; an analysis system; a configuration file, a function filter/the function filter comprising a trained neural network… …); thus, they do not integrate the abstract idea into a practical application. See MPEP 2106.05(f). Accordingly, this/these additional element(s) does/do not integrate the abstract idea into a practical application because it/they does/do not impose any meaningful limits on practicing the abstract idea. Again, the claim is directed to an abstract idea. The claim does not include additional elements that are sufficient to amount to significantly more than the judicial exception. As discussed above with respect to integration of the abstract idea into a practical application, the additional elements of using generic computer components (e.g., one or more processors; a memory coupled to the one or more processors, the memory include instructions executable by the one or more processors…; a configuration interpreter; an analysis system; a configuration file, a function filter/the function filter comprising a trained neural network…) to perform the steps/limitations amounts to no more than mere instructions to apply the exception using a generic computer component. Again, see MPEP 2106.05(f). For the above-mentioned reasons, viewed the claim as a whole, the additional elements/additional limitations individually and in combination do not integrate the identified abstract idea into a practical application. Furthermore, there is neither improvement to another technology or technical field nor an improvement to the functioning of the computer itself.
Independent claim 9 (step 2B): The additional element in claim 9 (e.g., one or more processors; a memory coupled to the one or more processors, the memory include instructions executable by the one or more processors…; a configuration interpreter; an analysis system; a configuration file, a function filter/the function filter comprising a trained neural network… …) is/are recited at a high level of generality and/or are recited as performing generic computer functions routinely used in the computer applications; thus, it is/they are not significantly more than the identified abstract idea. In other word, the additional elements “e.g., one or more processors; a memory coupled to the one or more processors, the memory include instructions executable by the one or more processors…; a configuration interpreter; an analysis system; a configuration file, a function filter/the function filter comprising a trained neural network…” is/are amounts no more than mere instructions to apply the judicial exception(s) of steps/limitations 1-6 mentioned above; thus, they are not significantly more than the identified abstract idea. See MPEP 2106.05(f). Therefore, for the above-mentioned reasons, viewed as a whole, even in combination, the above additional steps/additional elements/additional limitations do not amount to significantly more/do not provide an inventive concept. Furthermore, there is neither improvement to another technology or technical field nor an improvement to the functioning of the computer itself.
As per independent claims 2 and 16: Alice Corp. also establishes that the same/similar analysis should be used for all categories of claims. Therefore, a method claim 2 and a non-transitory computer readable medium claim 16 is also rejected as ineligible subject matter under 35 U.S.C. 101 for substantially the same/similar reasons as the system claim(s) 9. The additional components (i.e., a configuration interpreter; an analysis system; a function fitter comprising a trained neural network; a non-transitory computer readable medium storing instructions which, when executed by at least one processor, cause the at least one processor…..; a function fitter comprising a generative artificial intelligence (AI) model; a configuration file/the configuration file… ) described in independent claim 2, and/or claim 16 add nothing of substance to the underlying abstract idea. At best, the claim(s) 2 is merely providing an environment to implement the abstract idea.
Dependent claims 3-8, 10-15 and 17-21 are merely add further details of the abstract steps/elements recited in claims 2, 9 and 16 without including an improvement to another technology or technical field, an improvement to the functioning of the computer itself, or meaningful limitations beyond generally linking the use of an abstract idea to a particular technological environment. Looking at the limitations as an ordered combinations adds nothing that is not already present when looking at the elements taken individually. Furthermore, there is neither improvement to another technology or technical field nor an improvement to the functioning of the computer itself. Therefore, dependent claims 3-8, 10-15 and 17-21 are also non-statutory subject matter.
Prior Art that is pertinent to Applicant’s disclosure
7. The prior art made of record is considered pertinent to applicant's disclosure.
Paff et al; (US 2018/0365295 A1), wherein teaches Methods, systems, and apparatus include computer programs encoded on a computer-readable storage medium, including a method for providing content. One or more content items are identified that are eligible to be presented in response to received requests in accordance with one or more selection criteria. One of the content items includes one or more parameters that can be varied. A range is identified over which a parameter of the one or more parameters is variable, and a subset of the range is identified, wherein the subset is less than the entire range. A request for content is received that is satisfied by the content item. The content item is prepared, including randomly selecting a value for the parameter that is within the subset of the range. At least one performance aspect associated with the prepared one content item is monitored. Also see paras 0018-0036, 0040-0042, 0047, 0065, 0070 0085-0086.
Duke; (US 2019/0392487 A1), wherein teaches System, device, and method of automatic construction of digital advertisements. An Artificial Intelligence (AI) unit (e.g., neural network in paras 0025, 0052) is configured to receive as input: digital copies of past advertisements, and data of their performance results; as well as brand guidelines and a creative brief for automatic generation of a new advertisement. The AI unit generates a set of advertisement elements, such as logo, headline, a sub-headline, call-to-action, legal content, and an image; based on analysis of the input and detection that these particular advertisement elements correspond to previous performance results that are beyond a pre-defined threshold. An automatic advertisement generation unit generates a new advertisement by digitally placing the set of advertisement elements onto a canvas. Optionally, the system automatically generates on-the-fly in real-time a user-tailored advertisement, that is based on analysis of past performance of advertisements that were shown by the same advertiser to this particular end-user.
OKAMOTO; (US 2020/0293898 A1), wherein teaches A computer implemented method for generating and optimizing an artificial intelligence model, the method comprising receiving input data and labels, and performing data validation to generate a configuration file, and splitting the data to generate split data for training and evaluation; performing training and evaluation of the split data to determine an error level, and based on the error level, performing an action, wherein the action comprises at least one of modifying the configuration file and tuning the artificial intelligence model automatically; generating the artificial intelligence model based on the training, the evaluation and the tuning; and serving the model for production.
Vo et al; (US 2021/0334698 A1), wherein teaches An example method includes initializing a configuration file for a machine learning model, wherein the initializing is performed in response to receiving a request from a user, and wherein the configuration file comprises a plurality of sections that is configurable by the user, configuring at least one parameter of a feature engineering rules section of the configuration file, wherein the configuring the at least one parameter of the feature engineering rules section is based on a first value provided by the user, configuring at least one parameter of an algorithm definitions section of the configuration file, wherein the configuring the at least one parameter of the algorithm definitions section is based on a second value provided by the user, and populating the configuration file using the feature engineering rules section as configured and the algorithm definitions section as configured, to generate the machine learning model.
ZHANG et al; (US 2020/0349441 A1), wherein teaches A method of operating a neural network, comprising: at each input node of an input layer, weighting a respective input element received by that node by applying a first class of probability distribution, thereby generating a respective set of output parameters describing an output probability distribution; and from each input node, outputting the respective set of output parameters to one or more nodes in a next, hidden layer of the network, thereby propagating the respective set of output parameters through the hidden layers to an output layer; the propagating comprising, at one or more nodes of at least one hidden layer, combining the sets of input parameters and weighting the combination by applying a second class of probability distribution, thereby generating a respective set of output parameters describing an output probability distribution, wherein the first class of probability distribution is more sparsity inducing than the second class of probability distribution.
Ferguson et al; (US 2003/0149603 A1), wherein teaches A system and method for preprocessing input electronic commerce data to a non-linear model for use in an electronic commerce (e-commerce) system. The non-linear model includes parameters that define the representation of the e-commerce system, and operates in two modes: run-time and training. A data preprocessor preprocesses received data in accordance with predetermined preprocessing parameters, and outputs preprocessed data. The data preprocessor includes an input buffer for receiving and storing the input data. The input data may include one or more outlier values. A data filter detects and removes, and may optionally replace, any outlier values in the input data, generating corrected input data. An output device outputs the corrected data from the data filter as preprocessed data, which may be input to the non-linear model in training mode to train the non-linear model, and/or in run-time mode to generate control parameters and/or predictive output information for the e-commerce system.
Anthony et al; (US 2019/0228439 A1), wherein teaches Methods and systems are described for collecting response data, such as electroencephalography data, functional magnetic resonance imaging data, galvanic skin response data, heart rate data, body temperature data, eye tracking data, face tracking data, head tracking data, etc., as users receive a presentation of digital content and then utilizing that response data to dynamically produce or revise other digital content, such as advertisements, to elicit a specific user response and an expected engagement with the digital content, such as the advertisement.
Further, see additional references cited in PTO-892.
Conclusion
Any inquiry concerning this communication or earlier communications from the examiner should be directed to Thuy Nguyen whose telephone number is 571-272-4585 and fax number is 571-273-4585. The examiner can normally be reached on Mon-Thurs, 8:30 am to 5: 00 pm.
If attempts to reach the examiner by telephone are unsuccessful, the Examiner’s supervisor, Ilana Spar can be reached on 571-270-7537. The FAX number for the organization where this application or proceeding is assigned is 571-273-8300.
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/THUY N NGUYEN/
Primary Examiner, Art Unit 3622.
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