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
Status of the Claims
This action is in response to an application No. 19/179,175 filed on 4/15/2025. Claims 1-20 are pending. All pending claims are examined.
Continued Examination Under 37 CFR 1.114
This application is a continuation application of U.S. application No. 18/352,024 filed on 07/13/2023, now U.S. Patent 12,307,478 (“Parent Application”). See MPEP §201.07. In accordance with MPEP §609.02 A. 2 and MPEP §2001.06(b) (last paragraph), the Examiner has reviewed and considered the prior art cited in the Parent Application.
Also in accordance with MPEP §2001.06(b) (last paragraph), all documents cited or considered ‘of record’ in the Parent Application are now considered cited or ‘of record’ in this application. Additionally, Applicant(s) are reminded that a listing of the information cited or ‘of record’ in the Parent Application need not be resubmitted in this application unless Applicant(s) desire the information to be printed on a patent issuing from this application. See MPEP §609.02 A. 2.
Double Patenting
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 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); 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 nonstatutory double patenting provided the reference application or patent either is shown to be commonly owned with the examined application, or claims an invention made as a result of activities undertaken within the scope of a joint research agreement. See MPEP § 717.02 for applications subject to examination under the first inventor to file provisions of the AIA as explained in MPEP § 2159. See MPEP § 2146 et seq. for applications not subject to examination under the first inventor to file provisions of the AIA . A terminal disclaimer must be signed in compliance with 37 CFR 1.321(b).
The USPTO Internet website contains terminal disclaimer forms which may be used. Please visit www.uspto.gov/patent/patents-forms. The filing date of the application in which the form is filed determines what form (e.g., PTO/SB/25, PTO/SB/26, PTO/AIA /25, or PTO/AIA /26) should be used. A web-based eTerminal Disclaimer may be filled out completely online using web-screens. An eTerminal Disclaimer that meets all requirements is auto-processed and approved immediately upon submission. For more information about eTerminal Disclaimers, refer to www.uspto.gov/patents/process/file/efs/guidance/eTD-info-I.jsp.
Claims 1-20 are rejected on the ground of nonstatutory double patenting as being unpatentable over claims 1-20 of Patent No. 12,307,478.
Claims 1, 9, 17 are rejected on the ground of nonstatutory double patenting as being unpatentable over claims 1, 9, 17 of Patent No. 12,307,478. Although the claims at issue are not identical, they are not patentably distinct from each other because the reference claim anticipates the claims under examination.
Claims 2, 10, 18, are rejected on the ground of nonstatutory double patenting as being unpatentable over claims 1, 10 18 of Patent No. 12,307,478. Although the claims at issue are not identical, they are not patentably distinct from each other because the reference claim anticipates the claims under examination.
Claims 3, 11, 19 , are rejected on the ground of nonstatutory double patenting as being unpatentable over claims 3, 11, 19 of Patent No. 12,307,478. Although the claims at issue are not identical, they are not patentably distinct from each other because the reference claim anticipates the claims under examination.
Claims 4, 12, 20, are rejected on the ground of nonstatutory double patenting as being unpatentable over claims 4, 12, 20 of Patent No. 12,307,478. Although the claims at issue are not identical, they are not patentably distinct from each other because the reference claim anticipates the claims under examination.
Claims 5, 13, are rejected on the ground of nonstatutory double patenting as being unpatentable over claims 5, 13 of Patent No. 12,307,478. Although the claims at issue are not identical, they are not patentably distinct from each other because the reference claim anticipates the claims under examination.
Claims 6, 14, are rejected on the ground of nonstatutory double patenting as being unpatentable over claims 6, 14 of Patent No. 12,307,478. Although the claims at issue are not identical, they are not patentably distinct from each other because the reference claim anticipates the claims under examination.
Claims 7, 15, are rejected on the ground of nonstatutory double patenting as being unpatentable over claims 7,15 of Patent No. 12,307,478. Although the claims at issue are not identical, they are not patentably distinct from each other because the reference claim anticipates the claims under examination.
Claims 8, 16, are rejected on the ground of nonstatutory double patenting as being unpatentable over claims 8,16 of Patent No. 12,307,478. Although the claims at issue are not identical, they are not patentably distinct from each other because the reference claim anticipates the claims under examination.
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- 20 are rejected under 35 U.S.C. 101 because the claimed invention is directed to non-statutory subject matter.
Claims 1-20 are not compliant with 101, according with the last “2019 Revised Patent Subject Matter Eligibility Guidance” (2019 PEG), published in the MPEP 2103 through 2106.07(c). The Examiner’s analysis is presented below in all the claims.
Claim 1: Step 1 of 2019 PGE, does the claim fall within a Statutory Category? Yes. The claim recites a method.
Step 2A - Prong 1: Is a Judicial Exception recited in the claim? Yes. The claim recites the limitations of “generating impact data associated with a media experiment by: identifying an impact variable to be analyzed; querying, for each media experiment…the performance data for a period comprising the media experiment to generate the impact data, wherein the impact data is associated with each media experiment; generating … model including fixed effects and random effects, the …model comprising the impact data … and providing the …model as input … to train … to determine insights associated with the media experiment and the performance data”.
The “generating, identifying, providing ” limitations, as drafted, is a process that, under its broadest reasonable interpretation, covers performance of the limitations as certain methods of organizing human activity, advertising, marketing or sales activities or behaviors. The method for generating a media experiment dataset. Thus, the claim recites an abstract idea.
Step 2A - Prong 2: Integrated into a Practical Application? No. The claim recites additional limitations, such as, “retrieving performance data associated with the impact variable, wherein the performance data is associated with date, time, and a geographic region;” These are limitations toward accessing or receiving data (gathering data).
The Examiner analyses other supplementary elements in the claim in view of the instant disclosure:
“A computer-implemented; in a stored media experiment dataset ; a mixed model comprising a statistical model ; a machine learning algorithm comprising a random forest algorithm “. These elements are recited in a very generic way.
The Examiner gives the broadest reasonable interpretation to the above elements. They are insignificant extra-solution activity. See MPEP 2106.05(g).
The combination of these additional elements can also be considered no more than mere instructions “to apply” the exception, See MPEP 2106.05(f).
Accordingly, even in combination, these additional elements do not integrate the abstract idea into a practical application because they do not impose any meaningful limits on practicing the abstract idea.
The claim as a whole does not integrate the method of organizing human activity into a practical application. Thus, the claim is ineligible because is directed to the recited judicial exception (abstract idea).
Step 2B : claim provides an inventive concept? No.
As discussed with respect to Step 2A Prong Two, the supplementary or additional elements in the claim
“A computer-implemented; in a stored media experiment dataset ; a mixed model comprising a statistical model ; a machine learning algorithm comprising a random forest algorithm “, amount to no more than mere instructions to apply the exception. i.e., mere instructions to apply an exception using generic hardware and software cannot integrate a judicial exception into a practical application at Step 2A or provide an inventive concept in Step 2B.
Under the 2019 PEG, a conclusion that an supplementary or additional element is insignificant extra-solution activity in Step 2A should be re-evaluated in Step 2B.
Again, in this step, the additional elements in the claims under consideration are:
“A computer-implemented; in a stored media experiment dataset ; a mixed model comprising a statistical model ; a machine learning algorithm comprising a random forest algorithm “ , is considered to be extra-solution activity in Step 2A, and thus it is re-evaluated in Step 2B to determine if it is more than what is well-understood, routine, conventional activity in the field.
Other limitations in the claim, such as:
“retrieving performance data associated with the impact variable, wherein the performance data is associated with date, time, and a geographic region;” These are limitations toward accessing or receiving data (gathering data). Accessing data is very well understood, routine and conventional computer task activity; It represents insignificant extra solution activity. Mere data-gathering step[s] cannot make an otherwise nonstaturory claim statutory In re Grams,888 F.2d 835, 840 (Fed. Cir. 1989) (quoting In re Meyer, 688 F.2d 789, 794 (CCPA 1982)).
Further, the instant specification does not provide any indication that the additional elements
“A computer-implemented; in a stored media experiment dataset ; a mixed model comprising a statistical model ; a machine learning algorithm comprising a random forest algorithm “, is anything other than generic hardware, and the OIP Techs., Inc., v. Amazon.com, Inc., 788 F.3d 1359, 1363, 115 USPQ2d 1090, 1093 (Fed. Cir. 2015) (sending messages over a network); and v. Presenting offers and gathering statistics, OIP Techs., 788 F.3d at 1362-63, 115 USPQ2d at 1092-93; court decisions cited in MPEP 2106.05(d)(II) indicate that merely computer receives and sends information over a network and presenting or displaying information, is a well‐understood, routine, conventional function when it is claimed in a merely generic manner (as it is here).
Accordingly, a conclusion that the “A computer-implemented; in a stored media experiment dataset ; a mixed model comprising a statistical model ; a machine learning algorithm comprising a random forest algorithm “ limitations (pointed above) are well-understood, routine, conventional activity is supported under Berkheimer Option 2. The claim is ineligible.
Claim 9: Step 1 of 2019 PGE, does the claim fall within a Statutory Category? Yes. The claim recites a non-transitory computer-readable storage medium.
Step 2A - Prong 1: Is a Judicial Exception recited in the claim ? Yes. Because the same reasons pointed above.
Step 2A - Prong 2: Integrated into a Practical Application? No. Because the same reasons pointed above.
Step 2B : claim provides an inventive concept? No. Because the same reasons pointed above. The claim is ineligible.
Claim 17: Step 1 of 2019 PGE, does the claim fall within a Statutory Category? Yes. The claim recites a system.
Step 2A - Prong 1: Is a Judicial Exception recited in the claim ? Yes. Because the same reasons pointed above.
Step 2A - Prong 2: Integrated into a Practical Application? No. Because the same reasons pointed above.
Step 2B : claim provides an inventive concept? No. Because the same reasons pointed above. The claim is ineligible.
Dependent claims 2-8, 10-16 and 18-20, the claims recite elements such as “wherein the standardized query comprises one or more root words, one or more synonyms, and a target, and wherein the root words and synonyms are not changed between queries;” etc. These elements do not integrate the system of organizing human activity into a practical application. The claims are ineligible.
Claim Rejections - 35 USC § 103
In the event the determination of the status of the application as subject to AIA 35 U.S.C. 102 and 103 (or as subject to pre-AIA 35 U.S.C. 102 and 103) is incorrect, any correction of the statutory basis for the rejection will not be considered a new ground of rejection if the prior art relied upon, and the rationale supporting the rejection, would be the same under either status.
The following is a quotation of 35 U.S.C. 103 which forms the basis for all obviousness rejections set forth in this Office action:
A patent for a claimed invention may not be obtained, notwithstanding that the claimed invention is not identically disclosed as set forth in section 102 of this title, if the differences between the claimed invention and the prior art are such that the claimed invention as a whole would have been obvious before the effective filing date of the claimed invention to a person having ordinary skill in the art to which the claimed invention pertains. Patentability shall not be negated by the manner in which the invention was made.
The factual inquiries set forth in Graham v. John Deere Co., 383 U.S. 1, 148 USPQ 459 (1966), that are applied for establishing a background for determining obviousness under 35 U.S.C. 103 are summarized as follows:
1. Determining the scope and contents of the prior art.
2. Ascertaining the differences between the prior art and the claims at issue.
3. Resolving the level of ordinary skill in the pertinent art.
4. Considering objective evidence present in the application indicating obviousness or nonobviousness.
Claims 1, 4-6, 9, 12-14, 17 and 20 are rejected under 35 U.S.C. 103 as being unpatentable over US PG. Pub. No. 20140278771(Rehman) in view of US Patent No. 8010404 (Wu).
As to claims 1, 9 and 17, Rehman discloses a computer implemented method ( see Fig. 1 and associated disclosure also abstract) comprising:
a) generating impact data associated with a media experiment by: b) identifying an impact variable to be analyzed;
(“…embodiment of method 1421, each row corresponds to a economic market participant and in which the groupings specified by the predictive record define naturally targetable advertising groups with economic market participants of each advertising group predicted to react similarly to a common advertising campaign directed thereto”, paragraph 328.
“…Regardless, the GUI 1701 permits the user to experiment with their own dataset in a highly intuitive manner without even having to understand how the PREDICT command term operates, what inputs it requires, …”, paragraph 440 and 443.
“[0550] For instance, a user administrative page equivalent to those described above at FIGS. 19A, 19B, and 19C provide reporting capabilities through which a user may specify the input sources (e.g., the dataset of sales data for a customer organization), restrictions, filters, historical data, and other relevant data sources such as social media data, updated sales data, and so forth [an impact variable to be analyzed].
[0563] Social media data is available from sources including Radian 6 and Buddy Media offered by salesforce.com. Such sources provide aggregated and structured data gleaned from social media sources such as Facebook, Twitter, LinkedIn, and so forth. Using these sources, it may be possible to associate an individual, such as John Doe, with a particular sales opportunity and then enhance the indices with data that is associated with John Doe within the social networking space. For example, perhaps John Doe has tweeted about a competitors product or the products offered by the salesperson. Or their may be a news feed which mentions the product or the company or the sales opportunity targeted, or there may be customer reviews which are contextually relevant, and so forth….”, paragraphs 550 and 563 and Figs 19A-19C);
c) retrieving performance data associated with the impact variable, wherein the performance data is associated with date, time, and a geographic region; and
(“[0499] FIG. 19A depicts a specialized GUI 1901 to query using historical dates. The specialized GUI 1901 implementation depicted here enables users to filter on a historical value by comparing a historical value versus a current value in a multi-tenant database system. …”, paragraph 499. See also Fig. 19A “from …to…”.
“[0564] In certain embodiments, benchmarking capabilities are provided which enable a user to analyze supplemental data sources based on, for example, … data which is arranged by customers in geographical region, and so forth…”, paragraph 564);
d) querying, for each media experiment in a stored media experiment dataset,
(“[0443] Nevertheless, the minimum confidence threshold is specified at 1710 permits a lay user to experiment with their dataset in a highly intuitive manner…”, paragraph 443)
the performance data for a period comprising the media experiment to generate the impact data, wherein the impact data is associated with each media experiment;
([0566] In one embodiment, historical data is tracked and the scope of historical data that may be analyzed, viewed, and otherwise explored by a user is based on subscription terms. For instance, a cloud based service subscriber may expose the relevant user interface to all customers for free [Examiner interprets as media experiment], but then limit the scope of data that may be analyzed to only an exemplary three months, whereas paying subscribers get a much deeper and fuller dataset, perhaps two years worth of historical analysis[Examiner interprets as media experiment]. …. Conversely, the user interface described here permits the user to specify a historical date range which then enables the user to explore how data has changed over time or query the database in the perspective of a past date, resulting in query results returning the data as they were at the past date, rather than as they exist in the present. The methodologies described herein use a separate object to that database updates may be fully committed and further so that change and audit logs may be flushed without losing the historical data”, paragraph 566.
See also querying, for each media experiment in a stored media experiment dataset,
the performance data for a period comprising the media experiment to generate the impact data, paragraphs 431, 443 and Figs 17A and 17B);
e) generating a mixed model comprising a statistical model[ including fixed effects and random effects,]
([0015] FIG. 6 depicts a simplified flow for probabilistic modeling [Examiner interprets as generating a mixed model]”, paragraph 15.
“…For example, a CRM database may include a table that describes a customer with fields for basic contact information such as name, address, phone number, fax number, etc. Another table might describe a purchase order, including fields for information such as customer, product, sale price, date, etc. In some multi-tenant database systems, standard entity tables might be provided for use by all tenants. For CRM database applications, such standard entities might include tables for Account, Contact, Lead, and Opportunity data, each containing pre-defined fields…”, paragraph 108.
“[0133] FIG. 8 depicts an exemplary tabular dataset. With tabular data, each row contains information about one particular entity and each of the many rows are independent from one another. Each column contains a single type of information, and such data may be data typed as, for example, numerical, categorical, Boolean, etc. Column types may be mixed and matched within a table and the data type applied or assigned for any given column is uniform amongst all cells or fields within the entire column, but one column's data type does not restrict any particular data type on any other column….”, paragraph 133. See also paragraph 143. See Fig. 6 and Fig. 8 and associated disclosure);
the mixed model comprising the impact data and the media experiment dataset (see at least paragraphs 17A and 17B and associated disclosure.
“[0431] FIG. 17A depicts a Graphical User Interface (GUI) 1701 to display and manipulate a tabular dataset having missing values by exploiting a PREDICT command term. More particularly, a GUI is provided at a display interface to a user which permits the user to upload or specify a dataset having columns and rows and then display the dataset as a table and subject it to manipulation by populating missing values (e.g., null-values) with predicted values. At element 1707 the user specifies the data to be analyzed and displayed via the GUI 1701. For instance, the user may browse a local computing device for a file, such as an excel spreadsheet, and then upload the file to the system for analysis, or the user may alternatively specify a dataset which is accessible to the host organization which is providing the GUI 1701. For instance, the host organization is a cloud based service provider and where the user's dataset already resides within the cloud, the user can simply specify that dataset as the data source via the action at element 1707”, paragraph 431-432); and
f) providing the mixed model as input to a machine learning algorithm comprising a random forest algorithm to train the machine learning algorithm to determine insights associated with the media experiment and the performance data
(“… probabilistic modeling flow advances to element 603 … based on learning 607 derived from the defined space of possible outcomes 606. The flow then advances to element 604 where observed data is utilized by gathering information from available sources 608 which then loops back to learning at element 607 to recursively better inform the probabilistic model [Examiner interprets as providing the mixed model as input to a machine learning algorithm ]…”, paragraph 124 and Fig. 6.
“… an unsupervised cross-categorization learning technique is utilized for clustering based on MCMC inference in a novel nested nonparametric Bayesian model. This model can be viewed as a Dirichlet Process mixture over the dimensions or columns of Dirichlet process mixture models over sampled data points or rows….”, paragraph 147.
“…[0513] According to other embodiments, datasets are explored beyond the boundaries of any particular customer organization having data within the multi-tenant database system. For instance, in certain embodiments, benchmark predictive scores are generated based on industry specific learning using cross-organizational data stored within the multi-tenant database system. For example, data mining may be performed against telecom specific customer datasets, given their authorization or license to do so. Such cross-organization data to render a much larger multi-tenant dataset can then be analyzed via the analysis engine's models and provide insights, relationships, causations,..”, paragraph 513).
Rehman does not expressly disclose but Wu discloses
including fixed effects and random effects,
Wu, that is in the business for providing price and promotion response analysis (abstract). Wu teaches “A mixed-model framework addresses the need for both highly predictive models and the existence of an estimable model for each store and product. In a mixed-effect model, information (in the form of data history) is leveraged across all stores and products, and a single cohesive model is built. Stores and products with little or no information in their data history default to an "average" (fixed-effect) model. Likewise, stores and products with a wealth of information in their data history will end up with unique parameter estimates tailored to their response pattern, via estimation of non-zero store and/or product-specific adjustment factors (random effects) which are added to the fixed-effect portion of the model”, 28:28-40.
Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to incorporate Wu’s teaching with the teaching of Rehman. One would have been motivated to provide a mixed model framework comprising a statistical model using data default to fixed-effect and data adjustment random effect in order to analyze variable data.
As to claims 4, 12 and 20, Rehman discloses
wherein the impact variable comprises at least one of: quantity of sales, quantity of customers, or number of transactions.
(“…For instance, certain embodiments permit a user to query the indices via the PREDICT command term to ask a question such as: "Will an opportunity close AND at what amount?"[ impact variable] Such capabilities do not exist within conventionally available means”, paragraph 377).
As to claims 5, 6, 13 and 14, Rehman discloses
Claims 5 and 13:
wherein the period comprising the media experiment comprises performance data from six hours prior to, and six hours following the media experiment.
(“…The data set to compute this score consists of all the opportunities that have been closed (either won/loss) in a given period of time, such as 1, 2, or 3 years or a lifetime of an organization, etc., and such a duration may be configured using the date range controls of GUI 1901 to specify the date range, even if that range is in the past….”, paragraph 505 and Fig. 19. The Examiner notes that is obvious that the data range can be established in any amount of hours (i.e. 6 hours) and the results would have been predictable).
Claims 6 and 14:
wherein the return is a numerical score associated with an estimated efficacy of the at least one media experiment
(“[0064] FIG. 22A provides a chart depicting predictive relationships for opportunity scoring”, paragraph 64, element 2211 ).
Claims 2-3, 10-11 and 18-19 are rejected under 35 U.S.C. 103 as being unpatentable over US PG. PUB. No. 20140278771 (Rehman) in view of US Patent No. 8010404 (Wu) and in view of US Patent No. 9406077 (Zhao).
As to claims 2, 10 and 18, Rehman discloses
wherein the stored media experiment dataset is generated by performing a standardized query of a plurality of media sources for results associated with media mentions, and
(“Disclosed herein are systems and methods for rendering scored opportunities using a predictive query interface including means for receiving input from a user device specifying a dataset of sales data for a customer organization, in which the sales data specifies a plurality of sales opportunities; generating indices from rows and columns of the dataset, the indices representing probabilistic relationships between the rows and the columns of the dataset;…”, abstract.
“[0133] FIG. 8 depicts an exemplary tabular dataset. With tabular data, each row contains information about one particular entity and each of the many rows are independent from one another. Each column contains a single type of information, and such data may be data typed as, for example, numerical, categorical, Boolean, etc. Column types may be mixed and matched within a table and the data type applied or assigned for any given column is uniform amongst all cells or fields within the entire column [Examiner interprets as standardized query ]…”, paragraph 133 and Fig. 8);
Rehman does not disclose but Zhao discloses
wherein the standardized query comprises one or more root words, one or more synonyms, and a target, and wherein the root words and synonyms are not changed between queries.
(“…Keywords may be automatically generated for a content page using a plurality of methods and systems. In one implementation, the keywords may be all or a subset of words and phrases that appear on the web page. In another implementation, keywords may include synonyms of words that appear on the web page or root words…”, 2:45-62.
The Examiner notes in Zhao, is optional to change or not change keywords “…In another implementation, a subset of the second plurality of keywords contains an addition, deletion or change of a keyword. Based on this change, a report may be generated about an advertisement metric. A publisher may then add, delete, or change the keyword to the advertisement based on the advertisement metric…”, 1:45-55. “Systems and methods of the present solution are directed to a per-keyword scoring and attribution process and tool that allows an advertisement program to select an advertisement to display based on an individual keyword. The technology allows an advertiser to achieve per-keyword target control. The technology further allows an advertiser to predict or analyze the result of adding, deleting, editing or changing bid parameters for target keywords”, 2:20-30).
Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to incorporate Zhao’s teaching with the teaching of Rehman. One would have been motivated to provide functionality such as root words, synonyms and target keywords in order to facilitate an advertiser to predict or analyze campaign results (see Zhao 2:20-30).
As to claims 3, 11 and 19, Rehman discloses
wherein a validity assessment is performed on the results of the query, and
(“…the analysis engine is capable of always assigning a valid probability to each and every outcome within the defined outcome space, and each probability assigned represents the degree of "belief" or the analysis engine's assessment of probabilistic quality according to the models applied …”, paragraph 183. See also Fig. 1 elements 160, 180 and 185.
See also assessment of convergence in Fig. 10B and associated disclosure and “assessing business opportunities and scoring”, paragraph 164);
wherein the validity assessment comprises classifying each result based on whether the media mention associated with the result was a paid advertisement, and if so, determining that the result is invalid.
(“…scoring every row in the dataset according to its probabilistic similarity to the specified row, and then returning the rows and their respective scores according to the user's constraints or the constraints of an implementing GUI, if any such constraints are given [Examiner interprets as a constraint, media mention associated with the result was a paid advertisement]”, paragraph 342.
“[0375] FIG. 16A illustrates usage of the PREDICT command term in accordance with the described embodiments. More particularly, the embodiment shown illustrates use of classification and/or regression to query the indices using the PREDICT command term in which the input 1601 to the PREDICT command term fixes a subset of the columns and further in which the output 1602 predicts a single target column…”, paragraph 375.
“[0377] For instance, consider classification or regression in which all but one of the columns are used to predict a single target column. The analysis engine can render the prediction using a single target column or can render the prediction using a few target columns at the user's discretion. For instance, certain embodiments permit a user to query the indices via the PREDICT command term to ask a question such as: "Will an opportunity close AND at what amount?" Such capabilities do not exist within conventionally available means”, paragraph 377).
Claims 7 and 15 are rejected under 35 U.S.C. 103 as being unpatentable over US PG. PUB. No. 20140278771 (Rehman) in view of US Patent No. 8010404 (Wu) and in view of US PG. PUB. No. 20200082431 (Rajasekharan).
As to claims 7 and 15,
Rehman does not disclose but Rajasekharan discloses
determining, from the mixed model and for at least one media experiment in the media experiment dataset, an insight associated with the at least one media experiment,
(“…Each entry 510 may also indicate other types of metadata, such as an applied filter, a file type, Multipurpose Internet Mail Extensions (MIME) type, color palette, bit depth, rendering technique, background color, compression algorithm, aperture of camera used to create the image, contrast level of the image, primary hue of the image, lens information of the camera used to create the image, pixel information, saturation information, vibrance information, etc. Any and all of the properties discussed above may be treated [an insight associated with the at least one media experiment] as features if desired”, paragraph 60 and Fig. 5)
Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to incorporate Rajasekharan ’s teaching with the teaching of Rehman. One would have been motivated to provide functionality to estimate market saturation level using a predictive model in order to estimate with good accuracy.
Rehman does not disclose but Rajasekharan discloses
wherein the insights comprise estimated market saturation levels, a score associated with the identified category of media mentions, or predicted future efficacy of the media mentions.
(“… having layers that predict the contribution of each feature to the score of an asset. “Features” comprise any properties, content, or thematic similarities which may be shared between assets. Features may refer both to the file properties of an asset (e.g., resolution, file type, etc.) and to objects or concepts depicted within (or referenced by) the contents of an asset (e.g., a feature of “tree” may be found if there is a depiction of a tree within an image or video, a reference to a tree within text or audio, etc.). Features may therefore comprise file metadata, identifiable objects depicted within an image, a length of an audio or text file in time or words, shared textual content in a text file, extracted word embeddings of a text file, tags identified in an image file, convolution neural network encoding of an image (using transfer learning), an amount of saturation of an image, a primary hue of an image, etc [Examiner interprets as a media or advertisement]…”, paragraph 54.
“[0058] With the number and type of shared features known, asset scoring model 410 predicts a score for the new asset. Asset scoring model 410 may utilize high fidelity models like neural network regression and random forest regression techniques to predict asset scores…”, paragraphs 58-59 and Fig. 4).
Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to incorporate Sun’s teaching with the teaching of Rehman. One would have been motivated to provide functionality for using random forest algorithm, to estimated market saturation levels, a score associated with the identified category of media mentions in order to “to scoring digital content based on the manner in which the digital content has been accessed and/or utilized by an audience”, (Rajasekharan paragraph 1).
Claims 8 and 16 are rejected under 35 U.S.C. 103 as being unpatentable over US PG. PUB. No. 20140278771 (Rehman) in view of US Patent No. 8010404 (Wu) and in view of US PG. PUB. No. 20140025483 (Villars).
As to claims 8 and 16, Rehman does not disclose but Villars discloses
comprising: storing the media experiment dataset as structured data in a data management system, including: encrypting the media experiment dataset using an encryption algorithm; and
(“A method for measuring the effectiveness of an advertisement includes: storing, in a database, a plurality of consumer data entries, each being associated with a consumer and wherein each includes at least a plurality of characteristics and activity data; identifying a subset of the plurality of characteristics; encrypting, by a processor the subset of characteristics for each consumer data entry using a one-way encryption; transmitting, by a transmitter, the one-way encryption; receiving, by a receiver, advertising data entries, each being associated with a consumer and wherein each includes at least the encrypted subset of characteristics and a segment indicator; identifying a subset of consumer data entries that correspond to the advertising data entries based on the encrypted subset of characteristics; and analyzing the activity data for each consumer data entry in the subset to measure the effectiveness of an advertisement based on the corresponding segment indicators.”, abstract.
“[0028] FIG. 2 illustrates a method 200 for creating an encrypted data dataset, discussed in more detail below, for storage in the encrypted data database 112. …”, paragraph 28 and Fig. 2)
storing the media experiment dataset as an encrypted matrix.
“…storage in the encrypted data database 112…”, paragraph 28 and Fig. 2.
“[0037] The resulting data may be stored in the encrypted data database 112, illustrated in FIG. 3. The encrypted data database 112 may include a plurality of consumer data entries 302 (e.g., consumer data entries 302a, 302b, and 302c). Each consumer data entries 402 may include at least encrypted characteristic data 304…”, paragraph 37 and Figs. 2-3).
Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to incorporate Villars’s teaching with the teaching of Rehman. One would have been motivated to provide functionality for encrypting data in order to protect consumers privacy in the measuring of the effectiveness of advertisements, (see Villars paragraph1).
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure.
“Recommendation Systems: An Insight Into Current Development and Future Research Challenges”. IEEE. 2022.
“Research on recommendation systems is swiftly producing an abundance of novel methods, constantly challenging the current state-of-the-art. Inspired by advancements in many related fields, like Natural Language Processing and Computer Vision, many hybrid approaches based on deep learning are being proposed, making solid improvements over traditional methods. On the downside, this flurry of research activity, often focused on improving over a small number of baselines, makes it hard to identify reference methods and standardized evaluation protocols. Furthermore, the traditional categorization of recommendation systems into content-based, collaborative filtering and hybrid systems lacks the informativeness it once had. With this work, we provide a gentle introduction to recommendation systems, describing the task they are designed to solve and the challenges faced in research. Building on previous work, an extension to the standard taxonomy is presented, to better reflect the latest research trends, including the diverse use of content and temporal information. To ease the approach toward the technical methodologies recently proposed in this field, we review several representative methods selected primarily from top conferences and systematically describe their goals and novelty. We formalize the main evaluation metrics adopted by researchers and identify the most commonly used benchmarks. Lastly, we discuss issues in current research practices by analyzing experimental results reported on three popular datasets.”
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/MARIA V VANDERHORST/ Primary Examiner, Art Unit 3621
6/10/2026
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