DETAILED ACTION
Response to Amendment
The amendment filed on 7 January 2026 has been entered.
Claims 1-9, 12-20 are pending.
Claim 3 is cancelled.
Claims 1, 12, 17, 20 are amended.
Claims 1-2, 4-9, and 12-20 will be pending.
Response to Arguments
Applicant's arguments filed on 7 January 2026 have been fully considered, but they are not persuasive.
Applicant’s remarks, regarding the rejections of claims under 35 USC 101, have been fully considered.
Applicant submits that the features of Claims 1, 2, 4- 9, and 12-20 do not describe an abstract concept, or a concept similar to those found by the Courts to be abstract, such as a mental process.
Applicant submits claim elements "[a] server, comprising ... circuitry which ... executes at least one data processing operation on the acquired plurality of data records, to generate processed data ... the at least one data processing operation includes a data blurring operation, a data cleansing operation, or a data enrichment operation ... applies a trained machine learning (ML) model on the processed data ... generates analytics information associated with the processed data, based on the application of the trained ML model ... controls a display device to display the generated analytics information including the determined ratio," do not recite a judicial exception. Applicant submits that the human mind is not equipped to perform at least the above-recited features of independent Claim 1. Accordingly, the Applicant submits that at least the above-recited features of independent Claim 1 are inextricably tied to a machine and do not represent a mental process performed in the human mind or by pen and paper.
Applicant submits that independent Claim 1 provides a specific improvement in helping the content creators, content distributers, artists, end users, and/or advertisers to derive meaningful and valuable insights from the plurality of data records, increase an engagement between artists/content creators and listeners (or viewers), and effectively target listeners (or viewers) for advertisement campaigns to further enhance media content creation/distribution/sales business.
Applicant has shown a teaching in the Specification that describes how the technology is improved and has a practical implementation and has established a clear nexus between the claim language and the improvements to the technology. Further, the claimed solution is "necessarily rooted in computer technology in order to overcome a problem specifically arising in the realm of computer[s]." Therefore, viewed individually and as an ordered combination, independent Claim 1 resolves a technology- centric problem, and is therefore directed to patent-eligible subject matter.
Examiner notes Applicant’s arguments, as outlined above, are directed to newly amended claim limitations for which Examiner has not yet made a prima facie case for, rendering Applicant’s arguments moot.
Applicant’s remarks, regarding the rejections of claims under 35 USC 103, have been fully considered.
Applicant respectfully submits that the combination of Maccini, Maccini2, Stout, and Dorai-Raj does not teach, suggest, or render obvious at least, for example, the features of "executes at least one data processing operation on the acquired plurality of data records, to generate processed data ... the at least one data processing operation includes a data blurring operation, a data cleansing operation, or a data enrichment operation ... applies a trained machine learning (ML) model on the processed data," as recited in amended independent Claim 1.
Examiner notes Applicant’s arguments, as outlined above, are directed to newly amended claim limitations for which Examiner has not yet made a prima facie case for, rendering Applicant’s arguments moot.
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 .
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 1-2, 4-9, and 12-20 are rejected under 35 U.S.C. 101 because the claimed invention is directed to a judicial exception, abstract idea, without significantly more.
Step 1: This part of the eligibility analysis evaluates whether the claim(s) falls within any statutory
category. MPEP 2106.03:
According to the first part of the Alice analysis, in the instant case, the claims were determined
to be directed to one of the four statutory categories: an article of manufacture, a method/process (Claims 17-19), a machine/system/product (Claims 1-2, 4-9, 12-16, 20), and a composition of matter. Based on the claims being determined to be within of the four categories (i.e., process, machine, manufacture, or composition of matter), (Step 1), it must be determined if the claims are directed to a judicial exception (i.e., law of nature, natural phenomenon, and abstract idea).
Step 2A Prong One: This part of the eligibility analysis evaluates whether the claim(s) recites a judicial exception.
Regarding independent claims 1, 17, 20, the claims recite a judicial exception (i.e., an abstract idea enumerated in the 2019 PEG) without significantly more (Step-2A: Prong One). The applicant's claim limitations under broadest reasonable interpretation covers activities classified under mental processes - concepts performed in the human mind (including an observation, evaluation, judgment, opinion) (see MPEP § 2106.04(a)(2), subsection Ill) and the 2019 PEG. As evaluated below:
Claims 1, 17, 20:
“determine first information indicating a first number of times the media content is shared over a period of time, based on geo-location information included in at least one of demographic data fields or contextual data fields of the plurality of data records” (mental process of judgement)
“determine second information indicating a second number of times the media content is shared, via a content sharing application, based on the geo- location information included in the at least one of the demographic data fields or the contextual data fields” (mental process of judgement)
“determines a ratio of the determined first information and the determined second information” (mental process of judgement)
If the identified limitation(s) falls within at least one of the groupings of abstract ideas, it is
reasonable to conclude that the claim(s) recites an abstract idea in Step 2A Prong One.
Step 2A Prong Two: This part of the eligibility analysis evaluates whether the claim(s) as a whole integrates the recited judicial exception into a practical application of the exception. As evaluated below:
“acquires, from a plurality of electronic devices, a plurality of data records each including information about a plurality of data fields, wherein each of the plurality of data records corresponds to media content sharing interaction”
“executes at least one data processing operation on the acquired plurality of data records, to generate processed data”
“generates analytics information associated with the processed data”
“controls a display device to display the generated analytics information including the determined ratio”
These recitations are deemed insufficient to transform the judicial exception to a patentable invention because the recitation is directed to instructions for mere data gathering or data output, see MPEP 2106.05(g).
“applies a trained machine learning (ML) model on the processed data to”
“based on the application of the trained ML model”
The recitation is directed to mere instructions to implement an abstract idea on a computer, or
merely uses a computer as a tool to perform an abstract idea and are considered to adding the words "apply it" (or an equivalent) with the judicial exception, See MPEP 2106.05(f).
“wherein the at least one data processing operation includes a data blurrinq operation, a data cleansinq operation, or a data enrichment operation”
These recitations are deemed insufficient to transform the judicial exception to a patentable invention because the recitation is directed to instructions merely indicating a field of use or technological environment in which to apply a judicial exception, see MPEP 2106.05(h).
Accordingly, 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 when considered as an ordered combination and as a whole.
Step 2B: This part of the eligibility analysis evaluates whether the claim, as a whole, amounts to
significantly more than the recited exception, i.e., whether any additional element, or combination of
additional elements, adds an inventive concept to the claim. MPEP 2106.05.
First, the additional elements considered as part of the preamble and the additional elements
directed to the use of computer technology are deemed insufficient to transform the judicial exception
to a patentable invention to a patentable invention because they generally link the judicial exception to
the technology environment, see MPEP 2106.05(h).
Second, the additional elements directed to mere application of the abstract idea or mere instructions to implement an abstract idea on a computer are deemed insufficient to transform the judicial exception to a patentable invention to a patentable invention because the limitations generally apply the use of a generic computer and/or process with the judicial exception, see MPEP 2106.05(f).
Third, the claims are directed to instructions merely indicating a field of use or technological environment in which to apply a judicial exception. The courts have found these types of limitations insufficient to transform the judicial exception to a patentable invention, see MPEP 2106.05(g).
Lastly, the claims directed to data gathering activity as noted above, are deemed directed to an insignificant extra-solution activity. The courts have found these types of limitations insufficient to
qualify as "significantly more", see MPEP 2106.05(g).
Furthermore, when considering evidence in view of Berkheimer v. HP, Inc., 881 F.3d 1360, 1368, 125 USPQ2d 1649, 1654 (Fed. Cir. 2018), see USPTO Berkheimer Memorandum (April 2018). Examiner notes Berkheimer: Option 2 - A citation to one or more of the court decisions discussed in MPEP § 2106.05(d}(II} as noting the well understood, routine, conventional nature of the additional element (s) (e.g., limitations directed to mere data gathering):
The courts have recognized the following computer functions as well understood, routine, and conventional functions when they are claimed in a merely generic manner (e.g., at a high level of generality) or as insignificant extra-solution activity, see MPEP 2106.05(d).
The additional limitations, as analyzed, failed to integrate a judicial exception into a practical application at Step 2A and provide an inventive concept in Step 2B, per the analysis above. Thus, considering the additional elements individually and in combination and the claims as a whole, the additional elements do not provide significantly more than the abstract idea. This claim is not patent eligible. Therefore, in examining elements as recited by the limitations individually and as an ordered combination, as a whole, claims 1, 17, 20 do not recite what the courts have identified as "significantly more".
Furthermore, regarding dependent claims 2, 4-9, 12-16, which depend from claim 1, claim 18-19, which depend from claim 17, the claims are directed to a judicial exception (i.e., an abstract idea enumerated in the 2019 PEG, a law of nature, or a natural phenomenon) without significantly more as highlighted below in the claim limitations by evaluating the claim limitations under the Step2A and 2B:
Claims 2, 18:
Incorporates the rejections of claims 1, 17, respectively.
“wherein the plurality of data fields comprises at least one of: the demographic data fields related to users associated with the plurality of electronic devices, device data fields associated with the plurality of electronic devices, content metadata fields associated with the media content shared, the contextual data fields, interaction data fields related to the media content shared, or vehicular data fields”
These recitations are deemed insufficient to transform the judicial exception to a patentable invention because the recitation is directed to instructions merely indicating a field of use or technological environment in which to apply a judicial exception, see MPEP 2106.05(h).
Limitations directed to mere instructions indicating a field of use or technological environment in which to apply a judicial exception cannot integrate a judicial exception into a practical application at Step 2A or provide an inventive concept in Step 2B.
Claim 4:
Incorporates the rejection of claim 1.
“wherein the generated analytics information indicates demographic information of a plurality of users, an amount of the media content shared by the plurality of users, and information related to content metadata fields associated with the media content”
These recitations are deemed insufficient to transform the judicial exception to a patentable invention because the recitation is directed to instructions merely indicating a field of use or technological environment in which to apply a judicial exception, see MPEP 2106.05(h).
Limitations directed to mere instructions indicating a field of use or technological environment in which to apply a judicial exception cannot integrate a judicial exception into a practical application at Step 2A or provide an inventive concept in Step 2B.
Claim 5:
Incorporates the rejection of claim 1.
“controls the trained ML model to determine a time duration of the media content at which a majority of users” (mental process of judgement)
The recitation is directed to mere instructions to implement an abstract idea on a computer, or
merely uses a computer as a tool to perform an abstract idea and are considered to adding the words "apply it" (or an equivalent) with the judicial exception, See MPEP 2106.05(f).
“related to the plurality of data records, performs the media content sharing interaction”
These recitations are deemed insufficient to transform the judicial exception to a patentable invention because the recitation is directed to instructions merely indicating a field of use or technological environment in which to apply a judicial exception, see MPEP 2106.05(h).
“controls the analytics information including the determined time duration of the media content”
These recitations are deemed insufficient to transform the judicial exception to a patentable invention because the recitation is directed to instructions for mere data gathering or data output, see MPEP 2106.05(g).
Limitations directed to mere instructions to implement an abstract idea on a computer/using computer as a tool or directed to instructions merely indicating a field of use or technological environment in which to apply a judicial exception or directed to instructions for mere data gathering or data output cannot integrate a judicial exception into a practical application at Step 2A or provide an inventive concept in Step 2B.
Claim 6:
Incorporates the rejection of claim 5.
“extracts text information from a portion of the media content based on the determined time duration” (mental process of judgement)
The recitation is directed to mere instructions to implement an abstract idea on a computer, or
merely uses a computer as a tool to perform an abstract idea and are considered to adding the words "apply it" (or an equivalent) with the judicial exception, See MPEP 2106.05(f).
“controls the analytics information including the extracted text information”
These recitations are deemed insufficient to transform the judicial exception to a patentable invention because the recitation is directed to instructions for mere data gathering or data output, see MPEP 2106.05(g).
Limitations directed to mere instructions to implement an abstract idea on a computer/using computer as a tool or directed to instructions for mere data gathering or data output cannot integrate a judicial exception into a practical application at Step 2A or provide an inventive concept in Step 2B.
Claim 7:
Incorporates the rejection of claim 1.
“based on information related to a combination of the demographic data fields, content metadata fields, and vehicular data fields of the plurality of data records”
These recitations are deemed insufficient to transform the judicial exception to a patentable invention because the recitation is directed to instructions merely indicating a field of use or technological environment in which to apply a judicial exception, see MPEP 2106.05(h).
“wherein the circuitry further generates the analytics information”
These recitations are deemed insufficient to transform the judicial exception to a patentable invention because the recitation is directed to instructions for mere data gathering or data output, see MPEP 2106.05(g).
Limitations directed to instructions merely indicating a field of use or technological environment in which to apply a judicial exception or directed to instructions for mere data gathering or data output cannot integrate a judicial exception into a practical application at Step 2A or provide an inventive concept in Step 2B.
Claim 8:
Incorporates the rejection of claim 7.
“wherein information related to the vehicular data fields indicates at least one of: a state of a vehicle in which the media content sharing interaction performed, model of the vehicle, speed of the vehicle, geo-location information of the vehicle, or setting information associated with an infotainment device of the vehicle”
These recitations are deemed insufficient to transform the judicial exception to a patentable invention because the recitation is directed to instructions merely indicating a field of use or technological environment in which to apply a judicial exception, see MPEP 2106.05(h).
Limitations directed to mere instructions indicating a field of use or technological environment in which to apply a judicial exception cannot integrate a judicial exception into a practical application at Step 2A or provide an inventive concept in Step 2B.
Claim 9:
Incorporates the rejection of claim 1.
“based on information related to a combination of the demographic data fields, content metadata fields, and the contextual data fields of the plurality of data records”
These recitations are deemed insufficient to transform the judicial exception to a patentable invention because the recitation is directed to instructions merely indicating a field of use or technological environment in which to apply a judicial exception, see MPEP 2106.05(h).
“wherein the circuitry further generates the analytics information”
These recitations are deemed insufficient to transform the judicial exception to a patentable invention because the recitation is directed to instructions for mere data gathering or data output, see MPEP 2106.05(g).
Limitations directed to instructions merely indicating a field of use or technological environment in which to apply a judicial exception or directed to instructions for mere data gathering or data output cannot integrate a judicial exception into a practical application at Step 2A or provide an inventive concept in Step 2B.
Claim 12:
Incorporates the rejection of claim 1.
“determines a media source” (mental process of judgement)
The recitation is directed to mere instructions to implement an abstract idea on a computer, or
merely uses a computer as a tool to perform an abstract idea and are considered to adding the words "apply it" (or an equivalent) with the judicial exception, See MPEP 2106.05(f).
“associated with the shared media content, and geo-location information related to the determined media source, based on the application of the trained ML model on the processed data”
These recitations are deemed insufficient to transform the judicial exception to a patentable invention because the recitation is directed to instructions merely indicating a field of use or technological environment in which to apply a judicial exception, see MPEP 2106.05(h).
“controls the analytics information including the determined media source and the determined geo-location information”
These recitations are deemed insufficient to transform the judicial exception to a patentable invention because the recitation is directed to instructions for mere data gathering or data output, see MPEP 2106.05(g).
Limitations directed to mere instructions to implement an abstract idea on a computer/using computer as a tool or directed to instructions merely indicating a field of use or technological environment in which to apply a judicial exception or directed to instructions for mere data gathering or data output cannot integrate a judicial exception into a practical application at Step 2A or provide an inventive concept in Step 2B.
Claim 13:
Incorporates the rejection of claim 1.
“determines at least one of: an artist, a composer, or a podcaster of the media content and an amount of sharing interactions for the media content based on the application of the trained ML model” (mental process of judgement)
The recitation is directed to mere instructions to implement an abstract idea on a computer, or
merely uses a computer as a tool to perform an abstract idea and are considered to adding the words "apply it" (or an equivalent) with the judicial exception, See MPEP 2106.05(f).
“controls the analytics information including the determined at least one of: the artist, the composer, or the podcaster of the media content, and the determined amount of sharing interactions for the media content”
These recitations are deemed insufficient to transform the judicial exception to a patentable invention because the recitation is directed to instructions for mere data gathering or data output, see MPEP 2106.05(g).
Limitations directed to mere instructions to implement an abstract idea on a computer/using computer as a tool or directed to instructions for mere data gathering or data output cannot integrate a judicial exception into a practical application at Step 2A or provide an inventive concept in Step 2B.
Claims 14, 19:
Incorporates the rejections of claims 1, 17, respectively.
“generates one or more recommendations based on the application of the trained ML model on the generated analytics information” (mental process of judgement)
“applies the trained ML model on the generated analytics information”
The recitation is directed to mere instructions to implement an abstract idea on a computer, or
merely uses a computer as a tool to perform an abstract idea and are considered to adding the words "apply it" (or an equivalent) with the judicial exception, See MPEP 2106.05(f).
“controls the generated one or more recommendations”
These recitations are deemed insufficient to transform the judicial exception to a patentable invention because the recitation is directed to instructions for mere data gathering or data output, see MPEP 2106.05(g).
Limitations directed to mere instructions to implement an abstract idea on a computer/using computer as a tool or directed to instructions for mere data gathering or data output cannot integrate a judicial exception into a practical application at Step 2A or provide an inventive concept in Step 2B.
Claim 15:
Incorporates the rejection of claim 14.
“wherein the generated one or more recommendations indicate at least one of: a portion of the media content to be used for advertisement, a time period associated with the advertisement, a geolocation associated with the advertisement, text information to be used for the advertisement, another media content to be used for the advertisement, or a collaboration between one or more artists of the media content”
These recitations are deemed insufficient to transform the judicial exception to a patentable invention because the recitation is directed to instructions merely indicating a field of use or technological environment in which to apply a judicial exception, see MPEP 2106.05(h).
Limitations directed to mere instructions indicating a field of use or technological environment in which to apply a judicial exception cannot integrate a judicial exception into a practical application at Step 2A or provide an inventive concept in Step 2B.
Claim 16:
Incorporates the rejection of claim 14.
“wherein the generated one or more recommendations are related to advertisement and indicate at least one of: geo- location information, demographic information of users, a time period, a particular day of a month, vehicular information, weather information, or information related to one of the plurality of electronic devices, for the advertisement”
These recitations are deemed insufficient to transform the judicial exception to a patentable invention because the recitation is directed to instructions merely indicating a field of use or technological environment in which to apply a judicial exception, see MPEP 2106.05(h).
Limitations directed to mere instructions indicating a field of use or technological environment in which to apply a judicial exception cannot integrate a judicial exception into a practical application at Step 2A or provide an inventive concept in Step 2B.
The dependent claims as analyzed above, do not recite limitations that integrated the judicial exception into a practical application. In addition, the claim limitations do not include additional elements that are sufficient to amount to significantly more than the judicial exception (Step-2B). Therefore, the claims do not recite any limitations, when considered individually or as a whole, that recite what have the courts have identified as "significantly more", see MPEP 2106.05; and therefore, as a whole the claims are not patent eligible. As shown above, the dependent claims do not provide any additional elements that when considered individually or as an ordered combination, amount to significantly more than the abstract idea identified. Therefore, as a whole, the dependent claims do not recite what have the courts have identified as "significantly more" than the recited judicial exception. Therefore, claims 2, 4-9, 12-16, 18-19 are rejected under 35 U.S.C. 101 because the claimed invention is directed to a judicial exception and does not recite, when claim elements are examined individually and as a whole, elements that the courts have identified as "significantly more" than the recited judicial exception.
Claim Rejections - 35 USC § 103
The following is a quotation of 35 U.S.C. 103 which forms the basis for all obviousness rejections set forth in this Office action:
A patent for a claimed invention may not be obtained, notwithstanding that the claimed invention is not identically disclosed as set forth in section 102, if the differences between the claimed invention and the prior art are such that the claimed invention as a whole would have been obvious before the effective filing date of the claimed invention to a person having ordinary skill in the art to which the claimed invention pertains. Patentability shall not be negated by the manner in which the invention was made.
The factual inquiries for establishing a background for determining obviousness under 35 U.S.C. 103 are summarized as follows:
1. Determining the scope and contents of the prior art.
2. Ascertaining the differences between the prior art and the claims at issue.
3. Resolving the level of ordinary skill in the pertinent art.
4. Considering objective evidence present in the application indicating obviousness or nonobviousness.
This application currently names joint inventors. In considering patentability of the claims the examiner presumes that the subject matter of the various claims was commonly owned as of the effective filing date of the claimed invention(s) absent any evidence to the contrary. Applicant is advised of the obligation under 37 CFR 1.56 to point out the inventor and effective filing dates of each claim that was not commonly owned as of the effective filing date of the later invention in order for the examiner to consider the applicability of 35 U.S.C. 102(b)(2)(C) for any potential 35 U.S.C. 102(a)(2) prior art against the later invention.
Claims 1-2, 4-5, 7-9, 12, 17-18, 20 are rejected under 35 U.S.C. 103 as being unpatentable over Maccini et al. (U.S. Pre-Grant Publication No. 2019/0312941, hereinafter ‘Maccini'), in view of Maccini et al. (U.S. Pre-Grant Publication No. 2020/0134671, hereinafter 'Maccini2'), Stout et al. (U.S. Pre-Grant Publication No. 20190095504, hereinafter 'Stout'), and further in view of Dorai-Raj et al. (U.S. Pre-Grant Publication No. 20200226418, hereinafter 'Dorai-Raj').
Regarding claim 1 and analogous claims 17, 20, Maccini teaches A server, comprising: circuitry which: acquires, from a plurality of electronic devices, a plurality of data records each including information about a plurality of data fields ([0064] The invention is generally directed to the novel and unique system and method for cross channel in-car media consumption measurement and analysis. As generally shown in FIG. 1, this approach incorporates the use of a reference time base and a reference location base, both of which are immutably recorded in a form that can be independently verified. This time base and location base are then used to a plurality of data records each including information about a plurality of data fields record events which are captured through the vehicle head unit (VHU).; The time and location information included in that event, is then validated for integrity, including accuracy 102, this can be performed locally or remotely on a separate server.; [0067] This correlation may be configured through A server, comprising: circuitry which: acquires, from a plurality of electronic devices use of multiple sources for the same event, aggregation of multiple VHU time information sets, integration of co-located devices with a reference time, for example a smart phone with network time and other well-known techniques.),
wherein each of the plurality of data records corresponds to media content sharing interaction ([0062] A key tenet of the system described herein is acknowledging that information received from a vehicle entertainment system is partial in nature, in that such information does not convey the context of the experience of a vehicle's occupants for a media event. The combination of wherein each of the plurality of data records corresponds to media content sharing interaction verifiable proof of performance, through accurate time and location alignment combined with multiple media sources and contextual information provides a rich, accurate and immutable record of a vehicles occupants experiences.; [0064] The invention is generally directed to the novel and unique system and method for cross channel in-car media consumption measurement and analysis. As generally shown in FIG. 1, this approach incorporates the use of a reference time base and a reference location base, both of which are immutably recorded in a form that can be independently verified.);
executes at least one data processing operation on the acquired plurality of data records, to generate processed data, wherein the at least one data processing operation includes a data blurrinq operation, a data cleansinq operation, or a data enrichment operation ([0278] In some embodiments the metrics and data aggregations that result from the event data processing, matching and analytics pipeline 720, as shown in FIG. 7, may be stored in a data repository for an aggregate and integration module 750.; [0269] In some embodiments, event executes at least one data processing operation on the acquired plurality of data records information may be provided by an intermediate process, for example a set of VHU data may be aggregated and/or to generate processed data processed. This processing may include wherein the at least one data processing operation includes a data blurrinq operation anonymization of one or more of the information parameters provided by the VHU, for example the identity of the VHU, vehicle, location and the like. Further this processing may include providing quantized stream of such information, where for example the information set is broken into discrete time segments, for example 1 minute.; [0073] Events may be created or generated from or a data enrichment operation multiple sources including VHU and/or proxies thereof, broadcast and other content sources, one or more devices connected to a VHU, aggregation of VHU information sets, including those that have undergone one or more processing and/or configuration steps, and/or other sources.);
applies a trained machine learning (ML) model on the processed data to ([0158] In a number of embodiments, machine learning methods may be applied to sets of events, and the information there in, for example to create probability distributions for events, in whole or in part, or the likelihood thereof. This may include such techniques as clustering, classification, regression and role extraction in any arrangement.-):
generates analytics information associated with the processed data, based on the application of the trained ML model ([0214] The evaluation of event information to establish correlations, using time, location, vehicle characteristics, user experience variations, contextual information and other sources may be undertaken through configuring one or more machine learning techniques, including for example those employing probability distributions. In some embodiments, these techniques may include the use of Hilbert spaces and/or other multi-dimensional spaces to align events, their context and their accurate timing.); and
Maccini fails to teach determine first information indicating a first number of times the media content is shared over a period of time, based on geo-location information included in at least one of demographic data fields or contextual data fields of the plurality of data records; and determine second information indicating a second number of times the media content is shared, via a content sharing application, based on the geo- location information included in the at least one of the demographic data fields or the contextual data fields; determines a ratio of the determined first information and the determined second information; controls a display device to display the generated analytics information including the determined ratio.
Maccini2 teaches controls the generated analytics information including the determined ratio ([0042] Create reports, metrics and analytics suitable for multiple stakeholders, including supporting queries and requests from those stakeholders, some of which may be dynamic in nature. As shown in FIG. 3, in one embodiment, a software 300 can be employed by a computer and displayed on a monitor or other visual device. The software can present a user with a number of options, or filters. For example, a licensee of such data may controls the generated analytics information create a report configuration 302 by the selection, via online filters, certain variables 310-320 such as months 310, weeks 312, day-part 314 (e.g., Monday-Friday 6:00 am-8:00 pm), format 316, demographic group (e.g., females aged 25-54) 318, source 320 (AM/FM, satellite, PANDORA, etc.), and/or a market (e.g., Boston, Philadelphia, etc.) 322.).
Maccini and Maccini2 are considered to be analogous to the claimed invention because they are in the same field of machine learning. In view of the teachings of Maccini, it would have been obvious for a person of ordinary skill in the art to apply the teachings of Maccini2 to Maccini before the effective filing date of the claimed invention in order to provide system and methodology to measure media content consumed in a vehicle with contextual analysis, which is converted into analytics and metrics to meet industry demand for greater accuracy, finer granularity and real-time measurement/analysis of media content consumption (cf. Maccini2, [0007] There exists significant industry demand, from the stakeholders identified above, for a more comprehensive in-vehicle media consumption and use measurement system that can provide greater accuracy, finer granularity and real-time measurement/analysis of media content consumption across all applicable sources—such a system does not exist today.; [0008] To meet industry expectations, there is a need for such a system to be able to continuously provide measurement data in real-time and with a high degree of geographic location accuracy. A large sample size is also a pre-requisite of achieving this requirement.; [0009] Still further, having developed a system and methodology to actually measure the media content, including audio and video, consumed in a vehicle, there is also a demand for a differentiation between multiple users of the vehicle (e.g. members of the same family). This includes contextual analysis of how media consumption may differ with situation (e.g. a mother or father may primarily listen to adult news and music content during their commute while alone in the car but might listen to kids' channels whenever their children are in the car). This can further include the delivery of raw data to a third party organization, or the OEM or other users, to convert it into useful media analytics and metrics.).
Stout teaches determine first information indicating a first number of times the media content is shared over a period of time, based on geo-location information included in at least one of demographic data fields or contextual data fields of the plurality of data records; and determine second information indicating a second number of times the media content is shared, via a content sharing application, based on the geo- location information included in the at least one of the demographic data fields or the contextual data fields; determines a ratio of the determined first information and the determined second information ([0030] Read determines a ratio of the determined first information and the determined second information ratio data analytics may comprise but are not limited to: data pertaining to an amount of visitors/views, data pertaining to an amount (e.g. average) of completion of the digital presentation document, data pertaining to time (e.g. average) time spent viewing the digital presentation document, categorization of levels of readership by users (individually or collectively), charting and/or rich data objects related to determine first information indicating a first number of times the media content is shared over a period of time user activity (e.g. over a given time period) a number of times a specific user accesses the specific digital presentation content, determination (e.g. percentage-based) of a read rate and/or scroll rate, analysis of data selection/click log tracking for specific content/links, determine second information indicating a second number of times the media content is shared, via a content sharing application metrics related to sharing, liking, etc. of the specific digital presentation content, analysis of feedback/comments provided by users and categorization of user access to the digital presentation content through different application/services.);
controls a display device to display the generated analytics information including the determined ratio ([0015] As an example, exemplary data analytics, that are surfaced through a user interface, comprise read ratio data analytics. the generated analytics information including the determined ratio Exemplary read ratio data analytics provide a comprehensive analysis, for managers of digital content, regarding access to the digital content by other users. For ease of understanding the present disclosure, read ratio data analytics is an umbrella term that comprises a plurality multiple levels of analysis provide for user (e.g. author of the digital content) regarding access to the digital content by other users. While examples described herein may enable managers (e.g. authors) of digital content to receive multiple levels of analysis regarding access to their digital content, an exemplary application/service may be configured to initially controls a display device to display show, through a user interface, different levels of analysis of the read ratio data analytics.).
Maccini, Maccini2, and Stout are considered to be analogous to the claimed invention because they are in the same field of machine learning. In view of the teachings of Maccini and Maccini2, it would have been obvious for a person of ordinary skill in the art to apply the teachings of Stout to Maccini before the effective filing date of the claimed invention in order to assist an author to understand how engaged an audience is with content that the user manages (e.g. how popular it is, how specific users/groups of users interpret content, how much time users spend viewing the provided content, did the author land their message, etc.) (cf. Stout, [0017] Read ratio data analytics are configured to be quickly and easily digestible for a user, where a user can utilize exemplary read ratio data analytics to easily and efficiently comprehend consumption patterns related to content that is managed by the user. This can assist an author to understand how engaged an audience is with content that the user manages (e.g. how popular it is, how specific users/groups of users interpret content, how much time users spend viewing the provided content, did the author land their message, etc.).).
Dorai-Raj teaches determine first information indicating a first number of times the media content is shared over a period of time, based on geo-location information included in at least one of demographic data fields or contextual data fields of the plurality of data records ([0035] Component requests 114 can also include of the plurality of data records event data related to other information, such as information that a user of the client device has provided, based on geo-location information included in at least one of demographic data fields or contextual data fields geographic information indicating a state or region from which the component request was submitted, or other information that provides context for the environment in which the digital component will be displayed (e.g., a time of day of the component request, a day of the week of the component request, a type of device at which the digital component will be displayed, such as a mobile device or tablet device).; [0066] In some analytics environments, content known as “insights” can be provided for display to a user. Insights provide an analytical insight into user interaction data in which a user may be interested. For example, a pop-up window (or card) within a monthly review tab of a report that indicates a year-long snapshot of the over a period of time monthly progress of the metric in which the user is most interested can be presented as an insight. Metrics for insights can include whether the insight was served, how many times the insight was served, whether the insight was viewed, when the insight was viewed, whether insight was bookmarked for future reference, when the insight was bookmarked, whether the insight was discarded, when the insight was discarded, determine first information indicating a first number of times the media content is shared how many times the insight was shared, whether the insight was marked helpful, how many times the insight was marked helpful within a predetermined period of time (e.g., 30 days), how many times the insight was marked unhelpful within a predetermined period of time, or any actions available for interaction with the insight.); and
determine second information indicating a second number of times the media content is shared, via a content sharing application, based on the geo- location information included in the at least one of the demographic data fields or the contextual data fields ([0035] Component requests 114 can also include event data related to other information, such as information that a user of the client device has provided, based on the geo- location information included in the at least one of the demographic data fields or the contextual data fields geographic information indicating a state or region from which the component request was submitted, or other information that provides context for the environment in which the digital component will be displayed (e.g., a time of day of the component request, a day of the week of the component request, a type of device at which the digital component will be displayed, such as a mobile device or tablet device).; [0066] Metrics for insights can include whether the insight was served, how many times the insight was served, whether the insight was viewed, when the insight was viewed, whether insight was bookmarked for future reference, when the insight was bookmarked, whether the insight was discarded, when the insight was discarded, determine second information indicating a second number of times the media content is shared how many times the insight was shared, whether the insight was marked helpful, how many times the insight was marked helpful within a predetermined period of time (e.g., 30 days), how many times the insight was marked unhelpful within a predetermined period of time, or any actions available for interaction with the insight. These, and other metrics, can be via a content sharing application applied to other applications or content depending on the context of the content. There can be default values for each metric. In some implementations, default values can be user-specified.);
Maccini, Maccini2, Stout, and Dorai-Raj are considered to be analogous to the claimed invention because they are in the same field of machine learning. In view of the teachings of Maccini, Maccini2, and Stout, it would have been obvious for a person of ordinary skill in the art to apply the teachings of Dorai-Raj to Maccini before the effective filing date of the claimed invention in order to optimize relevant content to present to user, requiring less input and fewer computing resources (cf. Dorai-Raj [0012] Furthermore, leveraging this method of selecting content for users allows a new user to experience a similar utility and comfort level in interacting with a particular application or environment as users who are already familiar with the particular application or environment. This method allows content with which a user is presented to be optimized, and allows content to be provided to the user based on their everyday interactions. With this system, users do not have to do anything differently from what they already do to receive more relevant content—the method uses fewer processing resources because users do not have to separately provide feedback regarding content the user finds most relevant. In some applications or environments, users do not have any customizability options. Thus, the described techniques provide more relevant content to a user in an easier-to-use format while requiring less input and fewer computing resources than currently available methods.).
Regarding claim 2, Maccini, as modified by Maccini2, Stout, and Dorai-Raj, teaches The server of claim 1.
Maccini teaches wherein the plurality of data fields comprises at least one of: the demographic data fields related to users associated with the plurality of electronic devices ([0032] The invention has been developed to provide a new level of in-vehicle media consumption measurement capability achieving the following objectives: 8. Allow determination of vehicle demographic data fields related to users associated with the plurality of electronic devices user demographics by merging and cross-referencing available, known data (such as vehicle VIN and vehicle owner information) with other sources of third-party data (such as cell phone UDID and user data) to provide more comprehensive analysis of vehicle usage and operator demographics.),
device data fields associated with the plurality of electronic devices ([0073] Events may be created or generated from multiple sources including VHU and/or proxies thereof, broadcast and other content sources, one or more devices connected to a VHU, aggregation of VHU information sets, including those that have undergone one or more processing and/or configuration steps, and/or other sources.),
content metadata fields associated with the media content shared ([0025] Additionally, the invention is able to measure “cross channel” in-vehicle media consumption consistently and comparatively across multiple content metadata fields associated with the media content shared content types and sources (e.g. AM/FM radio, SDARS, internet radio, stored media, satellite video, terrestrial video, IP streaming video, ATSC 3.0 broadcasts, etc.).; [0027] Another key factor is the invention's ability to measure in-vehicle media consumption using a much larger sample size than ever before contemplated due to the architectural approach that fully supports low-cost, large-scale deployment in millions of vehicles.; [0028] Also important is the invention's ability to provide real-time dynamic measurement of in-vehicle media consumption (compared to the extensive lag time between survey and report of the existing methodologies). Alternatively, the system can permit for real-time or periodic monitoring of the use of audio, video, display content and related data in a vehicle, via software installed in the head unit of a vehicle along with hardware to receive the data, audio and video signals/channels.),
the contextual data fields ([0026] Also, of note is the invention's ability to provide not only better information on what content is being consumed, but incremental contextual data fields contextual information on how listeners respond to this content (such as changing station or skipping forward when they don't like what is playing, turning up the volume on favorite tracks, thumbs up, etc.). This incremental contextual information on how listeners respond to content for the first time provides the potential for a “feedback loop” to the creators/programmers of the applicable content (for example, allowing AM/FM radio stations to better understand how listeners respond to their broadcast, thus allowing them to enhance their programming to better meet their listener's preferences).; [0032] The invention has been developed to provide a new level of in-vehicle media consumption measurement capability achieving the following objectives: 6. Provide contextual data relating to the user's consumption behavior (such as turning up the volume during a favorite song, changing channel when the DJ is annoying, etc.)),
interaction data fields related to the media content shared ([0172] Sessions may include sets of events that represent a interaction data fields related to the media content shared user's interactions with vehicle media and/or communications systems, such as those involved in, listening, viewing, interacting, transacting, including a VHU. For example, this may include sources such as in car entertainment, communications, and other devices co-located in the vehicle at the time of the session. A session, for example, may have a start and end times, which are recorded in, at least one, reference time ledger.; [0174] Some of the event information that may comprise a session can include, for example: the sources of the media; the durations of the event; the volume or other audio control functions; any “edge” points where a user interaction occurs (e.g. Volume up/down; channel change; phone interrupt etc.); and/or the like.), or
vehicular data fields ([0032] The invention has been developed to provide a new level of in-vehicle media consumption measurement capability achieving the following objectives: 8. Allow determination of vehicle user demographics by merging and cross-referencing available, known data (such as vehicular data fields vehicle VIN and vehicle owner information) with other sources of third-party data (such as cell phone UDID and user data) to provide more comprehensive analysis of vehicle usage and operator demographics.).
Maccini, Maccini2, Stout, and Dorai-Raj are combinable for the same rationale as set forth above with respect to claim 1.
Regarding claim 4, Maccini, as modified by Maccini2, Stout, and Dorai-Raj, teaches The server of claim 1.
Maccini teaches wherein the generated analytics information indicates demographic information of a plurality of users ([0278] In some embodiments the metrics and data aggregations that result from the event data processing, matching and analytics pipeline 720, as shown in FIG. 7, may be stored in a data repository for an aggregate and integration module 750. The data then can be used to generated analytics information produce reports 770, 772, 774 for customers, which include summaries of metrics behaviors filtered, aggregated and organized according to parameters such as markets, media sources, indicates demographic information of a plurality of users demographics, time periods and time of day, which are of particular interest to specific customers and markets.),
an amount of the media content shared by the plurality of users, and information related to content metadata fields associated with the media content ([0278] The data then can be used to produce reports 770, 772, 774 for customers, which include summaries of metrics an amount of the media content shared by the plurality of users behaviors filtered, aggregated and organized according to parameters such as markets, media sources, demographics, time periods and time of day, which are of particular interest to specific customers and markets.; [0279] The resulting data may also be presented to customers and end users through data visualization dashboard software application on a desktop computer or mobile computing device. Such data visualization dashboard may include multiple options for slicing and dicing the information related to content metadata fields associated with the media content data results based on markets, media sources, demographics, time periods and time of day and so on. There may be different dashboards, with focus on particular subsets of resulting data, for different customer markets. For example, there may be vehicle OEM-focused dashboards, radio industry focused dashboards, advertising industry dashboards, music industry dashboards, etc.).
Maccini, Maccini2, Stout, and Dorai-Raj are combinable for the same rationale as set forth above with respect to claim 1.
Regarding claim 5, Maccini, as modified by Maccini2, Stout, and Dorai-Raj, teaches The server of claim 1.
Maccini teaches the circuitry further: controls the trained ML model to determine a time duration of the media content at which a majority of users, related to the plurality of data records, performs the media content sharing interaction ([0023] The instant approach incorporates the use of a determine a time duration of the media content at which a majority of users, related to the plurality of data records, performs the media content sharing interaction reference time base and a reference location base, both of which are immutably recorded in a form that can be independently verified. This time base and location base are then used to record events which are captured through the vehicle head unit (VHU).); and
Maccini2 teaches controls the analytics information including the determined time duration of the media content ([0020] The instant invention provides for means to deliver raw data, as well as controls the generated analytics information measurement data and analysis to auto manufacturing companies, providers of media content (including those available currently and others that may be available in the future), advertising companies, platforms and agencies, the music industry and other interested parties.; [0021] One objective of the invention is to measure all applicable forms of media consumption in an automobile and to provide an immutable including the determined time duration of the media content record of the time and location data associated with some or all of the measurements. This consumption will represent actual measured data rather than mere survey data (which is the only data available today). The actual measured data can then be delivered to end users to convert it into useful media analytics and metrics.).
Maccini, Maccini2, Stout, and Dorai-Raj are combinable for the same rationale as set forth above with respect to claim 1.
Regarding claim 7, Maccini, as modified by Maccini2, Stout, and Dorai-Raj, teaches The server of claim 1.
Maccini2 teaches wherein the circuitry further generates the analytics information based on information related to a combination of the demographic data fields ([0036] Such a data ingestion module 121 can include the ability to receive the raw data, ingest this information, via other modules discussed herein, and generates the analytics information based on information convert this information into formats, reports and representations suitable for use by the content providers, distributers and their value chain and other stakeholders, via a report generation module 123. Within this platform a series of modules may be employed to: For example, a licensee of such data may create a report configuration 302 by the selection, via online filters, certain variables 310-320 such as months 310, weeks 312, day-part 314 (e.g., Monday-Friday 6:00 am-8:00 pm), format 316, related to a combination of the demographic data fields demographic group (e.g., females aged 25-54) 318, source 320 (AM/FM, satellite, PANDORA, etc.), and/or a market (e.g., Boston, Philadelphia, etc.) 322. A licensee may make use of none, some, or all of the filters when creating a given report profile 302.),
content metadata fields ([0045] The platform 120 may include a software application that is loaded onto on a user's device which can require the user to opt in and agreed to the operation of the application and may provide information sets to the platform. These information sets may, in whole or in part be encrypted, either by the application or the platform, to protect the user's identity, and yet yield sufficient information to undertake analysis of the information to produce the measurements and metrics described herein. The platform may content metadata fields monitor content that is consumed on the device. This monitoring may be undertaken by using fingerprinting, watermarking, or other identification of content including metadata bound to the respective content.), and
vehicular data fields of the plurality of data records ([0053] Additional third party data can be appended, or added, to the media data that is collected. The additional third party data can include, but is not limited to, vehicular data fields of the plurality of data records motor vehicle registry information, mobile phone ID, location points of interest. In some embodiments, the mobile phone ID may be captured by mobile device pairing in the vehicle or as a result of location data collected from the vehicle and matching to various services that provide mobile phone location. For example, if a vehicle turns on each day at 8 AM at the same location, the system can assume that the location is a home address. Associating one or more mobile phones to a vehicle can allow more precise analysis of the consumer controlling media selection in a vehicle.).
Maccini, Maccini2, Stout, and Dorai-Raj are combinable for the same rationale as set forth above with respect to claim 1.
Regarding claim 8, Maccini, as modified by Maccini2, Stout, and Dorai-Raj, teaches The server of claim 7.
Maccini teaches wherein information related to the vehicular data fields indicates at least one of: a state of a vehicle in which the media content sharing interaction performed ([0088] In addition to the timing and location information, combinations of information representing a a state of a vehicle in which the media content sharing interaction performed state of a vehicle or a vehicle's infotainment head unit at the moment an event occurs can be represented as an information set, including event attributes such as those described herein.),
model of the vehicle ([0131] The sets of normalized and annotated events and the external data can then be used for processing, matching and analysis via module 720, which may include identification of experience sessions, such as start and end of listening sessions to a specific station, source or content piece (such as a song or a radio program), classification of the vehicle occupant based on location, vehicle make and model, experience behavior, etc. (or combinations of them), identification of behavioral patterns (movement, listening, driving and combinations of them), and generation of data aggregations 750 and measurements for sets of occupants by area, demographics, time and other data classification criteria.; [0247] This approach supports further matching with demographics and other location-specific data to be undertaken. For example, an events' model of the vehicle vehicle model, make, year, vehicle type and other vehicle characteristics can be matched with demographic data sets in order to associate the events with likely demographic profiles.),
speed of the vehicle, geo- location information of the vehicle ([0264] Sequences of events from the same vehicle can be used to identify the start and the end of a journey, for example from VHU provided (directly or indirectly) vehicle operating parameters, such as engine start, engine stop, speed of the vehicle velocity and the like. This journey may involve one or more experience for the occupant involving one or more media sources that may be determined to be experience sessions, such as listening to a radio station or making a phone call.; [0265] For example a specific sequences of events may be used to determine the start and end of a journey and may also include other engine ignition on and engine ignition off events, for example when filling a vehicle with fuel, stopping briefly at a specific destination (for example a school, shop and or the like), where these events may be integrated into a journey based, in part on the location information sets provided by a vehicle and/or geo- location information of the vehicle other location tracking systems (such as a cellphones GPS).), or
setting information associated with an infotainment device of the vehicle ([0088] In addition to the timing and location information, combinations of information representing a state of a vehicle or a vehicle's setting information associated with an infotainment device of the vehicle infotainment head unit at the moment an event occurs can be represented as an information set, including event attributes such as those described herein.).
Maccini, Maccini2, Stout, and Dorai-Raj are combinable for the same rationale as set forth above with respect to claim 1.
Regarding claim 9, Maccini, as modified by Maccini2, Stout, and Dorai-Raj, teaches The server of claim 1.
Maccini2 teaches wherein the circuitry further generates the analytics information based on information related to a combination of the demographic data fields ([0036] Such a data ingestion module 121 can include the ability to receive the raw data, ingest this information, via other modules discussed herein, and convert this information into formats, generates the analytics information based on information reports and representations suitable for use by the content providers, distributers and their value chain and other stakeholders, via a report generation module 123. Within this platform a series of modules may be employed to: For example, a licensee of such data may create a report configuration 302 by the selection, via online filters, certain variables 310-320 such as months 310, weeks 312, day-part 314 (e.g., Monday-Friday 6:00 am-8:00 pm), format 316, related to a combination of the demographic data fields demographic group (e.g., females aged 25-54) 318, source 320 (AM/FM, satellite, PANDORA, etc.), and/or a market (e.g., Boston, Philadelphia, etc.) 322. A licensee may make use of none, some, or all of the filters when creating a given report profile 302.),
content metadata fields ([0045] The platform 120 may include a software application that is loaded onto on a user's device which can require the user to opt in and agreed to the operation of the application and may provide information sets to the platform. These information sets may, in whole or in part be encrypted, either by the application or the platform, to protect the user's identity, and yet yield sufficient information to undertake analysis of the information to produce the measurements and metrics described herein. The platform may content metadata fields monitor content that is consumed on the device. This monitoring may be undertaken by using fingerprinting, watermarking, or other identification of content including metadata bound to the respective content.), and
the contextual data fields of the plurality of data records ([0036] Such a data ingestion module 121 can include the ability to receive the raw data, ingest this information, via other modules discussed herein, and convert this information into formats, reports and representations suitable for use by the content providers, distributers and their value chain and other stakeholders, via a report generation module 123. Within this platform a series of modules may be employed to: Compare the information to location based ephemera such as points of interest, roadways, commercial and residential zones, telecommunications facilities, city and county infrastructure and match to contextual data fields of the plurality of data records contextual information such as calendar events, weather patterns, time of day, week, season etc. and other pertinent contextual information.).
Maccini, Maccini2, Stout, and Dorai-Raj are combinable for the same rationale as set forth above with respect to claim 1.
Regarding claim 12, Maccini, as modified by Maccini2, Stout, and Dorai-Raj, teaches The server of claim 1.
Maccini teaches wherein the circuitry further: determines a media source, associated with the shared media content ([0032] The invention has been developed to provide a new level of in-vehicle media consumption measurement capability achieving the following objectives: 4. Provide the ability to measure content from multiple sources in a consistent and comparable way (to include broadcast or internet services such as video sources, AM/FM radio and SDARS, personalized services such as PANDORA, IHEART RADIO, HULU, NETFLIX, etc., stored media content such as CD, MP3 and DVD/Blu-ray players and determines a media source, associated with the shared media content content sourced from a connected CE device (including various platforms for in-vehicle smartphone integration such as APPLE CARPLAY, GOOGLE ANDROID AUTO, HARMAN AHA RADIO, PANASONIC AUPEO, PIONEER ZYPR, FORD SYNC, MIRRORLINK, AIRBIQUITY CHOREO, HULU, VIMEO, YOUTUBE, COX, COMCAST, VERIZON, NETFLIX, HBO, AMAZON PRIME, etc.).; 5. Provide more detailed metadata relating to what is actually consumed (such as song title, artist name, etc.). Such metadata may be achieved both through direct collection in the IVE and also through timestamp matching of the media source (e.g. a particular satellite radio channel) with a play list of the same content source captured separately.), and
geo-location information related to the determined media source, based on the application of the trained ML model on the processed data; and controls the analytics information including the determined media source and the determined geo-location information ([0268] In some embodiments, data from the identified listening experiences from multiple vehicles can be parsed and transformed into demographic and geo-location information related to the determined media source geographic based metrics that are commonly utilized by the audio industry into various forms of data products including data dashboard applications 770, data access APIs 772, and report generation 774, as shown in at least FIG. 7. The data can be based on the application of the trained ML model on the processed data output as a controls the analytics information including the determined media source and the determined geo-location information report 774, as shown in which may include such metrics as, but not limited to: average quarter hour (AQH), AQH Rating, unique number of listeners (CUME), CUME Rating, and time spent listening (TSL). Metrics for each audio source are calculated by examining the listening experience time, duration in seconds, geography and the vehicle's unique identity.).
Maccini, Maccini2, Stout, and Dorai-Raj are combinable for the same rationale as set forth above with respect to claim 1.
Regarding claim 18, Maccini, as modified by Maccini2, Stout, and Dorai-Raj, teaches The method of claim 17.
Maccini teaches wherein the plurality of data fields comprises at least one of: the demographic data fields related to users associated with the plurality of electronic devices ([0032] The invention has been developed to provide a new level of in-vehicle media consumption measurement capability achieving the following objectives: 8. Allow determination of vehicle demographic data fields related to users associated with the plurality of electronic devices user demographics by merging and cross-referencing available, known data (such as vehicle VIN and vehicle owner information) with other sources of third-party data (such as cell phone UDID and user data) to provide more comprehensive analysis of vehicle usage and operator demographics.),
device data fields associated with the plurality of electronic devices ([0073] Events may be created or generated from multiple sources including VHU and/or proxies thereof, broadcast and other content sources, one or more devices connected to a VHU, aggregation of VHU information sets, including those that have undergone one or more processing and/or configuration steps, and/or other sources.),
content metadata fields associated with the media content shared ([0025] Additionally, the invention is able to measure “cross channel” in-vehicle media consumption consistently and comparatively across multiple content metadata fields associated with the media content shared content types and sources (e.g. AM/FM radio, SDARS, internet radio, stored media, satellite video, terrestrial video, IP streaming video, ATSC 3.0 broadcasts, etc.).; [0027] Another key factor is the invention's ability to measure in-vehicle media consumption using a much larger sample size than ever before contemplated due to the architectural approach that fully supports low-cost, large-scale deployment in millions of vehicles.; [0028] Also important is the invention's ability to provide real-time dynamic measurement of in-vehicle media consumption (compared to the extensive lag time between survey and report of the existing methodologies). Alternatively, the system can permit for real-time or periodic monitoring of the use of audio, video, display content and related data in a vehicle, via software installed in the head unit of a vehicle along with hardware to receive the data, audio and video signals/channels.),
the contextual data fields ([0026] Also, of note is the invention's ability to provide not only better information on what content is being consumed, but incremental contextual data fields contextual information on how listeners respond to this content (such as changing station or skipping forward when they don't like what is playing, turning up the volume on favorite tracks, thumbs up, etc.). This incremental contextual information on how listeners respond to content for the first time provides the potential for a “feedback loop” to the creators/programmers of the applicable content (for example, allowing AM/FM radio stations to better understand how listeners respond to their broadcast, thus allowing them to enhance their programming to better meet their listener's preferences).; [0032] The invention has been developed to provide a new level of in-vehicle media consumption measurement capability achieving the following objectives: 6. Provide contextual data relating to the user's consumption behavior (such as turning up the volume during a favorite song, changing channel when the DJ is annoying, etc.)),
interaction data fields related to the media content shared ([0172] Sessions may include sets of events that represent a interaction data fields related to the media content shared user's interactions with vehicle media and/or communications systems, such as those involved in, listening, viewing, interacting, transacting, including a VHU. For example, this may include sources such as in car entertainment, communications, and other devices co-located in the vehicle at the time of the session. A session, for example, may have a start and end times, which are recorded in, at least one, reference time ledger.; [0174] Some of the event information that may comprise a session can include, for example: the sources of the media; the durations of the event; the volume or other audio control functions; any “edge” points where a user interaction occurs (e.g. Volume up/down; channel change; phone interrupt etc.); and/or the like.), or
vehicular data fields ([0032] The invention has been developed to provide a new level of in-vehicle media consumption measurement capability achieving the following objectives: 8. Allow determination of vehicle user demographics by merging and cross-referencing available, known data (such as vehicular data fields vehicle VIN and vehicle owner information) with other sources of third-party data (such as cell phone UDID and user data) to provide more comprehensive analysis of vehicle usage and operator demographics.).
Maccini, Maccini2, Stout, and Dorai-Raj are combinable for the same rationale as set forth above with respect to claim 1.
Claim 13 is rejected under 35 U.S.C. 103 as being unpatentable over Maccini, in view of Maccini2, Stout, Dorai-Raj, and further in view of Mattsson (U.S. Pre-Grant Publication No. 2020/0293538).
Regarding claim 13, Maccini, as modified by Maccini2, Stout, and Dorai-Raj, teaches The server of claim 1.
Maccini teaches wherein the circuitry further: determines at least one of: an artist, a composer, or a podcaster of the media content and an amount of sharing interactions for the media content based on the application of the trained ML model ([0032] The invention has been developed to provide a new level of in-vehicle media consumption measurement capability achieving the following objectives: 5. Provide more detailed metadata relating to what is actually consumed (such as song title, ; determines at least one of: an artist artist name, etc.).; [0021] The invention meets the above-identified needs by providing a system, apparatus, method and computer software for obtaining, measuring and analyzing in real-time (or on such other basis that can be configured) all forms of media content that a driver or passenger may consume inside of an automobile in combination with a reference time base and a reference location base, both of which are immutably recorded in a form that can be independently verified. This includes, but is not limited to, AM/FM radio, Satellite Digital Audio Radio Service (SDARS), stored media such as CDs, MP3s, DVDs and MP4s, content streaming, internet radio, audio books, a podcaster of the media content podcasts, text-to-speech content and other forms of content, including content routed to the In Vehicle Entertainment (IVE) system through integration with a smartphone, MP3 player, DVD/Blu-ray player, game console or other similar external Consumer Electronic (CE) device (via wired or wireless connectivity, including but not limited to USB, BLUETOOTH, Wi-Fi, etc.).); and
Maccini, as modified by Maccini2, Stout, and Dorai-Raj, fails to teach controls the analytics information including the determined at least one of: the artist, the composer, or the podcaster of the media content, and the determined amount of sharing interactions for the media content.
Mattsson teaches controls the analytics information including the determined at least one of: the artist, the composer, or the podcaster of the media content, and the determined amount of sharing interactions for the media content ([0078] In some implementations, using (418) the received data to provide the media recommendation includes controls the analytics information identifying (438) a user preference for the first media item based on the received data, and generating (440) the media recommendation in accordance with the user preference. As previously described, user preferences may be positive or negative (or neither/neutral) with respect to various aspects of a particular media item (e.g., musical characteristics of the media item, including the determined at least one of: the artist associated artists, associated albums/playlists, associated categories, and/or other composer associated properties of the media item). For example, the received data may indicate that the transition of the electronic device from the first playback mode to the second playback mode occurred during playback of the first media item by the electronic device (steps 430 through 434, FIG. 4C) (e.g., before finishing a song, transitioning from listening through headphones to listening through a speakerphone). In this example, a user switching from listening in a mode in which only the user can hear a particular song, to a mode in which others can also hear the song before it finishes, tends to suggest that the user intended to share the song for others to hear together. This may indicate a positive user preference (e.g., and the determined amount of sharing interactions for the media content the user wanted to share a song that the user likes) or a negative user preference (e.g., the user wanted to share the song for the purposes of collective ridicule). Whether the user preference is positive or negative, a corresponding media recommendation is provided (e.g., if positive, recommend songs by the same/similar artists; if negative, recommend songs from a different genre/artist).).
Maccini, Maccini2, Stout, Dorai-Raj, and Mattsson are considered to be analogous to the claimed invention because they are in the same field of machine learning. In view of the teachings of Maccini, Maccini2, Stout, and Dorai-Raj, it would have been obvious for a person of ordinary skill in the art to apply the teachings of Mattsson to Maccini before the effective filing date of the claimed invention in order to provide media recommendations based at least in part on implicit user behavior (cf. Mattsson, [0005] Accordingly, there is a need for systems and methods for providing media recommendations based at least in part on implicit user behavior. By using data associated with user behavior that implicitly corresponds to media playback, which data and user behavior exclude explicit user inputs for a media item (e.g., user inputs for controlling playback or providing user feedback), content providers are able to provide media recommendations that are more likely to be relevant to a user. Such systems and methods optionally complement or replace conventional methods for providing media recommendations.).
Claims 6, 14-16, 19 are rejected under 35 U.S.C. 103 as being unpatentable over Maccini, in view of Maccini2, Stout, Dorai-Raj, and further in view of Liongosari et al. (U.S. Pre-Grant Publication No. 2017/0053032, hereinafter ‘Liongosari').
Regarding claim 6, Maccini, as modified by Maccini2, Stout, and Dorai-Raj, teaches The server of claim 5.
Maccini, as modified by Maccini2, Stout, and Dorai-Raj, fails to teach wherein the circuitry further: extracts text information from a portion of the media content based on the determined time duration; and controls the analytics information including the extracted text information.
Liongosari teaches wherein the circuitry further: extracts text information from a portion of the media content based on the determined time duration ([0034] The super-platform 108 may also execute other analysis module(s) that perform other types of analysis regarding the data 104, including but not limited to data combination, data correlation, mathematical and/or statistical analysis, analysis to identify trends and/or patterns in the data 104, analysis that employs machine learning techniques, semantic and/or natural language based analysis of extracts text information from a portion of the media content text data, image and/or audio data analysis, or other types of processing.; [0037] In some implementations, an individual platform 102 may provide metadata with the data 104 that is ingested into the super-platform 108. In examples where an individual platform 102 does not provide metadata, or provides incomplete metadata, the super-platform 108 may generate metadata for the ingested data 104. This generation of metadata may be through a natural language (NL) or semantic analysis of the ingested data 104. In some examples, the super-platform 108 may generate metadata such as location tags or time tags for the ingested data 104, e.g., based on other information indicating a location of the platform 102 or a based on the determined time duration time when the metadata was generated or received.); and
controls the analytics information including the extracted text information ([0045] The aggregate data 118 may be accessed by a recommendation engine 120, which may controls the analytics information including the extracted text information analyze the aggregate data 118 to generate recommendation(s) 122 for the particular end-user 116 that is associated with the data 104 and/or aggregate data 118. In some examples, the recommendation(s) 122 may be for a group, class, category, and/or type of end-users that share common characteristic(s). The recommendation(s) 122 may include recommendation(s) regarding media (e.g., video, music, games, etc.) that the recommendation engine 120 predicts the end-user 116 may enjoy based on the aggregate data 118 for the end-user 116. Recommendation(s) 122 may also include recommendation(s) regarding products and/or services to purchase, businesses and/or other locations to visit, friend and/or follow suggestions for social networking, and so forth. One or more of the data 104, the aggregate data 118, or the recommendation(s) 122 may be stored on the super-platform 108, or on external device(s) accessible to the super-platform 108 over one or more networks.).
Maccini, Maccini2, Stout, Dorai-Raj, and Liongosari are considered to be analogous to the claimed invention because they are in the same field of machine learning. In view of the teachings of Maccini, Maccini2, Stout, and Dorai-Raj, it would have been obvious for a person of ordinary skill in the art to apply the teachings of Liongosari to Maccini before the effective filing date of the claimed invention in order to generate fewer, but more focused and accurate, recommendations than traditional platform, such that the recommendation engine of the super-platform may consume less storage, memory, and processing power (cf. Liongosari, [0007] Implementations provide the following advantages. By determining recommendations based on data from multiple platforms of different types, implementations provide more accurate recommendations for a user than those provided by traditional platforms that may have access to data from a single platform and/or a single type of platform. By providing more accurate recommendations, implementations may also provide technical advantages over traditional platforms. For example, implementations may generate fewer, but more focused and accurate, recommendations than traditional platform, such that the recommendation engine of the super-platform may consume less storage, memory, processing power, and/or other computing resources compared to traditional recommendation systems.).
Regarding claim 14, Maccini, as modified by Maccini2, Stout, and Dorai-Raj, teaches The server of claim 1.
Maccini, as modified by Maccini2, Stout, and Dorai-Raj, fails to teach wherein the circuitry further: applies the trained ML model on the generated analytics information; generates one or more recommendations based on the application of the trained ML model on the generated analytics information; and controls the generated one or more recommendations.
Liongosari teaches wherein the circuitry further: applies the trained ML model on the generated analytics information; generates one or more recommendations based on the application of the trained ML model on the generated analytics information; and controls the generated one or more recommendations ([0016] Implementations of the present disclosure include systems, devices, methods, and computer-readable media for receiving data generated by multiple platforms of different types, and determining recommendations for end-user(s) of the multiple platforms based on an analysis of the received data. An end-user may interact with multiple individual (e.g., siloed) platforms of different types or that support different business purposes or industries. The individual platforms may generate data describing, and/or resulting from, these interactions with end-user(s). The data from the various individual platforms may be applies the trained ML model on the generated analytics information received, ingested, stored, analyzed, and/or otherwise processed by a super-platform. In some implementations, the data may be aggregated to generate aggregate data. The data and/or aggregate data may be generates one or more recommendations based on the application of the trained ML model on the generated analytics information analyzed by a recommendation engine executing on the super-platform. The recommendation engine may controls the generated one or more recommendations determine one or more recommendations for a particular end-user based on an analysis of the data and/or aggregate data associated with that end-user.).
Maccini, Maccini2, Stout, Dorai-Raj, and Liongosari are combinable for the same rationale as set forth above with respect to claim 6.
Regarding claim 15, Maccini, as modified by Maccini2, Stout, Dorai-Raj, and Liongosari, teaches The server of claim 14.
Maccini2 teaches wherein the generated one or more recommendations indicate at least one of ([0018] The invention meets the above-identified needs by providing a system, apparatus, method and computer software for obtaining, measuring and analyzing in real-time (or on such other basis that can be configured) all forms of media content that a driver or passenger may consume inside of an automobile in combination with a reference time base and a reference location base, both of which are immutably recorded in a form that can be independently verified.):
a portion of the media content to be used for advertisement ([0030] The platform 120 is capable of generating various types of outputs, including audience measurement 130 a, advertising attribution 130 b, advertising analytics 130 c, source content analytics 130 d, auto OEM metrics 130 e, out of home advertising metrics 130 f, video analytics 130 g, vehicle user types 130 h, event tracking metrics 130 i, and/or other metrics analytics and metrics 130 j. Thus, an adaptive platform 120 is provided which can receive data from a variety of sources, tie such data to an immutable record, process such data, and produce actionable output data sets 130 a-j.; [0061] Listening data can be reported and analyzed in real time or on a historical basis. For one to one content, e.g. internet radio, this data can be used to a portion of the media content to be used for advertisement target content including advertising to listeners. For example, a listener that is within a certain distance of an advertiser's location or has visited an advertiser location in the past, can be specifically targeted.),
a time period associated with the advertisement ([0065] The content may additionally include targeted with the advertisement advertising based on known user behaviors, such as when a user is approaching the home, or work, a regular drop off, such as school or similar and/or at a time period associated specific times, such as when an aggregation of vehicles is stationary on a road network and the like.),
a geolocation associated with the advertisement ([0061] Listening data can be reported and analyzed in real time or on a historical basis. For one to one content, e.g. internet radio, this data can be used to target content including advertising to listeners. For example, a listener that is within a certain distance of an advertiser's a geolocation associated with the advertisement location or has visited an advertiser location in the past, can be specifically targeted.),
text information to be used for the advertisement, another media content to be used for the advertisement ([0069] A return on investment of an advertising campaign can be computed by comparing the cost of the advertising campaign relative to the number of store visits, and by calculating the total monetary value of what the vehicle owner/consumer actually spent at the store as a result of the advertising heard in the vehicle. Examining the impact of advertising delivered to a vehicle may include, but is not be limited to: (i) the impact of different ad creative text information to be used for the advertisement copy, content, ad length and other ad factors on vehicle/consumer behavior (ii) impact on different demographic groups of vehicle owners and drivers, (iii) the impact of advertising at different times of day, (iv) the impact of advertising in comparison to listener activity/store visits prior to and post advertising campaign run dates, (v) impact based on prior vehicle behavior and advertiser brand preferences, (vi) determining whether an ad was heard or seen in a vehicle, (vii) real time analysis, tracking and ad delivery, (viii) segmenting vehicles based on prior behavior including which stores frequented, (ix) assessment of the effectiveness of the another media content to be used for the advertisement content being delivered, or (x) assessment of the sequence of advertisements delivered and the like.), or a collaboration between one or more artists of the media content.
Maccini, Maccini2, Stout, Dorai-Raj, and Liongosari are combinable for the same rationale as set forth above with respect to claim 6.
Regarding claim 16, Maccini, as modified by Maccini2, Stout, Dorai-Raj, and Liongosari, teaches The server of claim 14.
Maccini2 teaches wherein the generated one or more recommendations are related to advertisement and indicate at least one of: geo- location information ([0068] Assessing whether the consumer drove to the advertiser's store or accessed the advertiser's website due to hearing or seeing an ad utilizing a geo- location information vehicles geographic location is a basis for advertisers and ad agencies to measure the effectiveness of an advertising campaign. The geographic location and exact time stamp of content such as an ad can be derived from the platform and may be cross referenced with data from programming logs of audio sources such as AM/FM radio stations, internet radio channels and satellite radio channels or other content sources and metadata derived from the invention.),
demographic information of users ([0053] In addition, demographic information of users demographic, psychographic, social, and other data can be widely available based on mobile phone ID's or telephone numbers. This added data can more specifically inform users of the data allowing better targeting of content including song and ad selection.),
a time period ([0035] To create meaningful analytics and metrics for the content providers, distributers and their stakeholders, a set or raw information which may include, the source of the content, the time period time period over which it was consumed, any available device operations information, any available and accessible user identity information and the locations at which the content was consumed can be ingested, processed, and output to meet the needs of those content providers and their stakeholders through a platform.),
a particular day of a month ([0036] Such a data ingestion module 121 can include the ability to receive the raw data, ingest this information, via other modules discussed herein, and convert this information into formats, reports and representations suitable for use by the content providers, distributers and their value chain and other stakeholders, via a report generation module 123. Within this platform a series of modules may be employed to: Compare the information to location based ephemera such as points of interest, roadways, commercial and residential zones, telecommunications facilities, city and county infrastructure and match to contextual information such as particular day of a month calendar events, weather patterns, time of day, week, season etc. and other pertinent contextual information.),
vehicular information ([0120] This information may be overlaid with POI relative to the advertisements, recommendations or other promotional materials that are communicated to the vehicle during the time period being monitored. In this manner, a vehicle journey may be considered as the relationship between the advertising provided at a specific time, the consumption of that information by the occupants of the vehicle and the relevant response of the occupants to that advertisement, such as stopping at a POI associated with that advertisement.; [0121] Further, if at another time a vehicle that has been exposed to a number of expressions of that advertisement or promotion, makes a journey to a POI, this may be evaluated and correlated to that advertisement. The platform may maintain a set of vehicle to POI relationships, where the location information is sufficient to identify that the vehicular information vehicle location and POI location intersected at a specific time, and yet the identity of the journey's start and end of the vehicle were only able to be identified to the degree required to place them in the market being analyzed.),
weather information ([0036] Such a data ingestion module 121 can include the ability to receive the raw data, ingest this information, via other modules discussed herein, and convert this information into formats, reports and representations suitable for use by the content providers, distributers and their value chain and other stakeholders, via a report generation module 123. Within this platform a series of modules may be employed to: Compare the information to location based ephemera such as points of interest, roadways, commercial and residential zones, telecommunications facilities, city and county infrastructure and match to contextual information such as calendar events, weather information weather patterns, time of day, week, season etc. and other pertinent contextual information.), or
information related to one of the plurality of electronic devices, for the advertisement ([0070] In such examples, information regarding the content, time, location, identifiers associated with a vehicle, user and/or information related to one of the plurality of electronic devices device may contribute, to the generation of exposure and/or attribution metrics, including those created in real time or near real time. At least one of the information sets can be communicated to the platform in real time, such that feedback to a stakeholder, e.g., a store, advertiser, content provider, may be provided to them in an actionable format.; [0071] This data can also assist in better targeting ads to consumers. The foregoing also can be measured by results recorded in ad attribution conversion rates outlined above. These metrics can be generated by the platform based on the information received from at least one device in a vehicle. This vehicle device provided information may be correlated with other sources, based on time, location, content or other informing aspects to create ad magnetism and/or other metrics.).
Maccini, Maccini2, Stout, Dorai-Raj, and Liongosari are combinable for the same rationale as set forth above with respect to claim 6.
Regarding claim 19, Maccini, as modified by Maccini2, Stout, and Dorai-Raj, teaches The method of claim 17.
Maccini, as modified by Maccini2, Stout, and Dorai-Raj, fails to teach further comprising: applying the trained ML model on the generated analytics information; generating one or more recommendations based on the application of the trained ML model on the generated analytics information; and controlling the generated one or more recommendations.
Liongosari teaches further comprising: applying the trained ML model on the generated analytics information; generating one or more recommendations based on the application of the trained ML model on the generated analytics information; and controlling the generated one or more recommendations ([0016] Implementations of the present disclosure include systems, devices, methods, and computer-readable media for receiving data generated by multiple platforms of different types, and determining recommendations for end-user(s) of the multiple platforms based on an analysis of the received data. An end-user may interact with multiple individual (e.g., siloed) platforms of different types or that support different business purposes or industries. The individual platforms may generate data describing, and/or resulting from, these interactions with end-user(s). The data from the various individual platforms may be applying the trained ML model on the generated analytics information received, ingested, stored, analyzed, and/or otherwise processed by a super-platform. In some implementations, the data may be aggregated to generate aggregate data. The data and/or aggregate data may be generating one or more recommendations based on the application of the trained ML model on the generated analytics information analyzed by a recommendation engine executing on the super-platform. The recommendation engine may controlling the generated one or more recommendations determine one or more recommendations for a particular end-user based on an analysis of the data and/or aggregate data associated with that end-user.).
Maccini, Maccini2, Stout, Dorai-Raj, and Liongosari are combinable for the same rationale as set forth above with respect to claim 6.
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure.
Ali et al. (NPL: “Machine Learning Technologies for Secure Vehicular Communication in Internet of Vehicles: Recent Advances and Applications”) teaches reviewing analytical modeling for offloading mobile edge-computing decisions based on machine learning and Deep Reinforcement Learning (DRL) approaches for the Internet of Vehicles (IoV).
Applicant's amendment necessitated the new ground(s) of rejection presented in this Office action. Accordingly, THIS ACTION IS MADE FINAL. See MPEP § 706.07(a). Applicant is reminded of the extension of time policy as set forth in 37 CFR 1.136(a).
A shortened statutory period for reply to this final action is set to expire THREE MONTHS from the mailing date of this action. In the event a first reply is filed within TWO MONTHS of the mailing date of this final action and the advisory action is not mailed until after the end of the THREE-MONTH shortened statutory period, then the shortened statutory period will expire on the date the advisory action is mailed, and any extension fee pursuant to 37 CFR 1.136(a) will be calculated from the mailing date of the advisory action. In no event, however, will the statutory period for reply expire later than SIX MONTHS from the date of this final action.
Any inquiry concerning this communication or earlier communications from the examiner should be directed to MAGGIE MAIDO whose telephone number is (703) 756-1953. The examiner can normally be reached M-Th: 6am - 4pm.
Examiner interviews are available via telephone, in-person, and video conferencing using a USPTO supplied web-based collaboration tool. To schedule an interview, applicant is encouraged to use the USPTO Automated Interview Request (AIR) at http://www.uspto.gov/interviewpractice.
If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, Michael Huntley can be reached on (303) 297-4307. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
Information regarding the status of published or unpublished applications may be obtained from Patent Center. Unpublished application information in Patent Center is available to registered users. To file and manage patent submissions in Patent Center, visit: https://patentcenter.uspto.gov. Visit https://www.uspto.gov/patents/apply/patent-center for more information about Patent Center and https://www.uspto.gov/patents/docx for information about filing in DOCX format. For additional questions, contact the Electronic Business Center (EBC) at 866-217-9197 (toll-free). If you would like assistance from a USPTO Customer Service Representative, call 800-786-9199 (IN USA OR CANADA) or 571-272-1000.
/MM/Examiner, Art Unit 2129
/MICHAEL J HUNTLEY/Supervisory Patent Examiner, Art Unit 2129