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 .
This office action is in response to communication filed on 8/1/2025.
Claims 2-21 are presented for examination.
Claim Rejections - 35 USC § 112
The following is a quotation of 35 U.S.C. 112(b):
(b) CONCLUSION.—The specification shall conclude with one or more claims particularly pointing out and distinctly claiming the subject matter which the inventor or a joint inventor regards as the invention.
The following is a quotation of 35 U.S.C. 112 (pre-AIA ), second paragraph:
The specification shall conclude with one or more claims particularly pointing out and distinctly claiming the subject matter which the applicant regards as his invention.
Claims 2 is rejected under 35 U.S.C. 112(b) or 35 U.S.C. 112 (pre-AIA ), second paragraph, as being indefinite for failing to particularly point out and distinctly claim the subject matter which the inventor or a joint inventor (or for applications subject to pre-AIA 35 U.S.C. 112, the applicant), regards as the invention.
Claim 2: on line 3 recites “electronic advertisements”, it’s not clear if the electronic advertisements are the same electronic advertisements recited on lines 6-7. Correction is required. For purpose of examination, the claims will be examined as the electronic advertisements on lines 3 and 6-7 are the same electronic advertisements.
Claim 7: on line 2 recites “one machine learning model”, it’s not clear if the machine learning model is the same machine learning model recited of independent claim 2, line 2. Correction is required. For purpose of examination, the claim will be examined as if the machine learning model is the same machine learning model of claim 2.
Claim 16: on line 2 recites “one machine learning model”, it’s not clear if the machine learning model is the same machine learning model recited of independent claim 15, line 9. Correction is required. For purpose of examination, the claim will be examined as if the machine learning model is the same machine learning model of claim 15.
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 2-21 are rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more.
Step 1: Determining that a claim falls within one of the four enumerated categories of patentable subject matter recited in 35 U.S.C. 101 (i.e., process, machine, manufacture, or composition of matter). (MPEP 2106.03)
Claims 2-7 and 19 recite a series of steps, thus falling within one of the four statutory classes; i.e., a process. Claims 8-14 and 20 describe tangible system components, thus falling within one of the four statutory classes; i.e., machine. Claims 15-18 and 21 describe non-transitory computer readable medium, thus falling within one of four statutory classes; i.e. manufacture.
Step 2A, Prong One: Evaluating whether the claim(s) recite(s) a judicial exception, i.e. whether a law of nature, natural phenomenon, or abstract idea is set forth or described in the claim. (MPEP 2106.04).
Representative claim 2 recites:
advertisements having associated scores and the associated scores characterizing a relevancy between the advertisements and a defined query; and to ranking a set of advertisements based on at least one feature vector, the at least one feature vector characterizing input data that includes flight details of a mobile craft, while the mobile craft is in transit, and wherein the at least one feature vector is defined or re-defined while the mobile craft is in transit.
The claims as drafted, is a process that, under its broadest reasonable interpretation, covers performance of the limitations in the mind but for the recitation of generic computer components. That is, other “Machine learning model” nothing in the claim elements precludes the steps from practically being performed in the mind.
For example, but for the reciting “Machine learning model”, “computer” in the context of this claim encompasses actions that a human could perform; e.g., a human ranking which ads are more relevant to people on airplanes based on their behavior. If a claim limitation, under its broadest reasonable interpretation, covers performance of the limitation in the mind but for the recitation of generic computer components, then it falls within the “Mental Processes” grouping of abstract ideas. Accordingly, the claim recites an abstract idea.
In addition, the limitations mentioned above (i.e., “characterizing” and “applying”, in the context of this claim) as drafted, are processes that, under their broadest reasonable interpretations, exemplify managing personal behavior.
That is, other than reciting “Machine learning model”, the steps of “characterizing” and “applying”, in the context of this claim encompasses steps of ranking which ads are more relevant to people on airplanes based on their behavior.
If a claim limitation, under its broadest reasonable interpretation, covers managing personal behavior, then it falls within the “Certain methods of organizing human activity” grouping of abstract ideas. Accordingly, the claim recites an abstract idea.
Independent claims 8 and 15 recite the same abstract idea as identified above and dependent claims 3-7, 9-14 and 16-21 further narrow it.
Step 2A, Prong Two: Identifying whether there are any additional elements recited in the claim beyond the judicial exception(s); and then evaluating those additional elements individually and in combination to determine whether they integrate the exception into a practical application. Prong Two distinguishes claims that are "directed to" the recited judicial exception from claims that are not "directed to" the recited judicial exception. (MPEP 2106.04).
This judicial exception is not integrated into a practical application. In particular, the claims recite the following additional elements:
machine learning model, computer (claims 2, 8 and 15);
A non-transitory computer-readable storage medium (claim 8);
Memory (claims 8);
Processors, interface (claims 8 and 15);
The machine learning model, computer, non-transitory computer-readable storage medium, Memory, Processors and interface are recited at a high-level of generality (i.e., as generic processors) such that they amount no more than mere instructions to apply the exception using generic computer components. They are no more than a tool to perform the ranking, applying and characterizing steps, and are considered as “apply it” as the claim invokes the computer as a tool to perform the abstract idea. See MPEP 2106.05(f)(2) (similar to Apple, Inc. v Ameranth and Intellectual Ventures I LLC v Capital One Bank (USA).
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. (MPEP 2106.05(f) Mere Instructions To Apply An Exception).
Regarding the limitations machine learning model, computer, non-transitory computer-readable storage medium, Memory, Processors and interface, as seen above, this limitation has been interpreted as “apply it”. However, this limitation can be additionally interpreted as insignificant extra-solution activity. As such, this limitation alone and in combination, does not integrate the abstract idea into a practical application because it does not impose any meaningful limits on practicing the abstract idea. (MPEP 2106.05(g) Insignificant Extra-Solution Activity).
Therefore, under Step 2A, Prong Two, the claims are directed to an abstract idea.
Step 2B: Identifying whether there are any additional elements (features/limitations/steps) recited in the claim beyond the judicial exception(s), and then evaluating those additional elements individually and in combination to determine whether they contribute an inventive concept (i.e., amount to significantly more than the judicial exception(s)). (MPEP 2106.05)
The claims do not include additional elements that are sufficient to amount to significantly more than the judicial exception. As discussed above with respect to integration of the abstract idea into a practical application, the additional elements of machine learning model, computer, non-transitory computer-readable storage medium, Memory, Processors and interface, alone and in combination amount to no more than mere instructions to apply the exception using generic computer components.
Mere instructions to apply an exception using a generic computer component cannot provide an inventive concept.
Regarding the limitations of machine learning model, computer, non-transitory computer-readable storage medium, Memory, Processors and interface; it is noted that sending information over a network has been recognized in the courts as being Well Understood Routine and Conventional (see MPEP 2106.05(d)(II) - i. Receiving or transmitting data over a network, e.g., using the Internet to gather data, Symantec, 838 F.3d at 1321, 120 USPQ2d at 1362 (utilizing an intermediary computer to forward information); TLI Communications LLC v. AV Auto. LLC, 823 F.3d 607, 610, 118 USPQ2d 1744, 1745 (Fed. Cir. 2016) (using a telephone for image transmission); OIP Techs., Inc., v. Amazon.com, Inc., 788 F.3d 1359, 1363, 115 USPQ2d 1090, 1093 (Fed. Cir. 2015) (sending messages over a network); buySAFE, Inc. v. Google, Inc., 765 F.3d 1350, 1355, 112 USPQ2d 1093, 1096 (Fed. Cir. 2014) (computer receives and sends information over a network).
Therefore, these additional elements do not amount to significantly more than a judicial exception and cannot provide an inventive concept. (MPEP 2106.05(d) Well-Understood, Routine, Conventional Activity).
Therefore, claims 2-21 are not patent eligible.
Claim Rejections - 35 USC § 102
In the event the determination of the status of the application as subject to AIA 35 U.S.C. 102 and 103 (or as subject to pre-AIA 35 U.S.C. 102 and 103) is incorrect, any correction of the statutory basis (i.e., changing from AIA to pre-AIA ) for the rejection will not be considered a new ground of rejection if the prior art relied upon, and the rationale supporting the rejection, would be the same under either status.
The following is a quotation of the appropriate paragraphs of 35 U.S.C. 102 that form the basis for the rejections under this section made in this Office action:
A person shall be entitled to a patent unless –
(a)(1) the claimed invention was patented, described in a printed publication, or in public use, on sale, or otherwise available to the public before the effective filing date of the claimed invention.
Claims 1-4, 6-9 and 11-21 are rejected under 35 U.S.C. 102(a)(1) as being anticipated by Obrien et al.(WO 2021/201831 Obrien hereinafter).
With respect to claim 2 Obrien teaches:
A computer-implemented method for ranking electronic advertisements, the computer- implemented method comprising (Abstract).
executing a learn-to-rank algorithm to train a machine learning model on a training dataset that includes electronic advertisements with associated scores characterizing a relevancy between the electronic advertisements and a defined query (see using machine learning model 114 for targeting electronic advertisements 116, each targeted electronic advertisement 116 may be associated with one or more target characteristics, metadata, keywords etc.)(see Figure 1 and paragraph 0021);
applying the trained machine learning model to rank a set of electronic advertisements based on a feature vector characterizing input data that includes flight details of an aircraft (targeted electronic advertisements 116 based on itinerary information 128 which includes aircraft information which may be received from aircraft computer systems over an aircraft data bus that the server 110 is in communication with)(Figure 1 and paragraph 0027).
With respect to claims 8 and 15, Obrien teaches memory and computer executable instructions to implement:
a machine learning engine having a training stage and an inference stage, wherein the inference stage is configured to, based on at least one machine learning model, rank a set of electronic advertisements based on a feature vector characterizing input data that includes flight details of an aircraft (targeted electronic advertisements 116 based on itinerary information 128 which includes aircraft information which may be received from aircraft computer systems over an aircraft data bus that the server 110 is in communication with)(Figure 1 and paragraph 0027).
With respect to claim 3, Obrien further teaches the flight details of the mobile craft describe a departure location of the mobile craft, a destination of the mobile craft, or a combination thereof (examples of aircraft information include one or more current location of the aircraft, a current route (source and/or destination etc. aircraft origin and an aircraft destination)(paragraph 0027) .
With respect to claim 4, Obrien further teaches wherein the input data further includes behavior data associated with a passenger of the mobile craft (interest information 130).
With respect to claim 6, Obrien teaches generating an advertisement index that includes the ranking of the set of electronic advertisements; and selecting an electronic advertisement from the set of electronic advertisements based on the ranking for presentation to a passenger on the mobile craft (matching some or all of the information of the user profile 124 with some or all the target characteristics in order to rank the highest advertisements based on the highest match of user characteristics to the advertisements(paragraph 0021).
With respect to claim 7, Obrien teaches wherein the applying the trained machine learning model to rank the set of electronic advertisements is performed independent of the selecting the electronic advertisement (selecting some of the information of the profile to the advertisements to present to the users, which might not be the highest ranking)(paragraph 0021).
With respect to claim 9, Obrien further teaches wherein inference stage is configured to rank the set of electronic advertisements based on an ensemble of machine learning models, and wherein the at least one machine learning model is comprised within the ensemble of machine learning models (see machine learning model within targeted advertisement delivery subsystem)(Figures 1A, 1B, 1C and 2).
With respect to claim 11, Obrien further teaches wherein the trained machine learning model is applied while the mobile craft is in transit (i.e. the server 110 may receive the information prior to takeoff of the aircraft 110, or alternatively, when the aircraft 100 is in the air (figure 1 and paragraph 0031).
With respect to claim 12, Obrien teaches wherein the input data further includes behavior data regarding a first passenger from the plurality of passengers independent of a second passenger from the plurality of passengers (interest information 130).
With respect to claim 13, Obrien further teaches an advertisement engine configured to select an electronic advertisement from the set of electronic advertisements based on the ranking by the machine learning engine, wherein the electronic advertisement is selected for presentation to a passenger of the mobile craft, wherein the advertisement engine is coupled to an infotainment system of the mobile craft via a satellite communication, and wherein the selected electronic advertisement is presented to the passenger via the infotainment system (see server side 110 on Figure 1C).
With respect to claim 14, Obrien further teaches wherein the training stage and the inference stage of the machine learning engine are executed independent of selection operations by the advertisement engine (see computer provider environment 170 on Figure 1C).
With respect to claim 16, Obrien teaches wherein one or more weights of the at least one machine learning model are adjusted based on historic engagement of an audience with the electronic advertisements during a previous flight (i.e. user profile 234 may be created and updated based on previous electronic activities of the user while onboard the aircraft 200, such as movies or music consumed by the user, advertisements interacted with by the user, websites visited by the user, etc. In some examples, the user profile 234 of the user is created and updated based on previous electronic activities of the user while onboard multiple aircraft which are included in a fleet of aircraft. )(Figure 2 and paragraph 0061).
With respect to claims 17-18, Obrien further teaches wherein the inference stage is further configured to, based on a second machine learning model, generate a second ranking of the set of electronic advertisements in order of the relevancy between the electronic advertisements and the at least one feature vector, wherein the at least one machine learning model is trained on a first training dataset that includes first relevancy scores determined by a first function, and wherein the second machine learning model is trained on a second training dataset that includes second relevancy scores determined by a second function (using machine learning model 114 for targeting electronic advertisements 116, each targeted electronic advertisement 116 may be associated with one or more target characteristics, metadata, keywords etc.)(see Figure 1 and paragraph 0021); and wherein the second function is defined in accordance with a second advertisement objective (targeted electronic advertisements 116 based on itinerary information 128 which includes aircraft information which may be received from aircraft computer systems over an aircraft data bus that the server 110 is in communication with)(Figure 1 and paragraph 0027).
With respect to claims 19-21, Obrien further teaches wherein displaying the ranked set of electronic advertisements on a user display based on at least one of: similarly ranked electronic advertisements, passenger group characteristics, and individual passenger characteristics (see Figure 4, 402; figure 5, 502; figure 6, 602; figure 7, 702 and figure 8, 802).
Claim Rejections - 35 USC § 103
In the event the determination of the status of the application as subject to AIA 35 U.S.C. 102 and 103 (or as subject to pre-AIA 35 U.S.C. 102 and 103) is incorrect, any correction of the statutory basis (i.e., changing from AIA to pre-AIA ) for the rejection will not be considered a new ground of rejection if the prior art relied upon, and the rationale supporting the rejection, would be the same under either status.
The following is a quotation of 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.
Claims 5 and 10 are rejected under 35 U.S.C. 103 as being unpatentable over Obrien.
Claims 5 and 10 further recite associated scores by a function of historic audience interactions that resulted in an engagement of a service offered by the electronic advertisements or purchase of a product offered by the electronic advertisements. Obrien teaches interest information 130 describes propensity to consume certain types of content or perform certain types of activities on Figure 1 and paragraph 0028. Obrien is silent as to associate a score of the user’s purchase of the offered product in the electronic advertisements. Official Notice is taken that is old and well known to associate a score based on audience interactions. It would have been obvious for a person of ordinary skill in the art to have included associating a score to the user’s purchases based on coupon redemption , in order to better rank/score the ads based on what the users are engage in or purchasing.
References cited but not applied;
Wright (2007/0156887) teaches on Figure 16 data structure 1600 may include multiple ad/query features 1610-1 through 1610-N, with a "total number of ad selections" 1620, a total "good" predictive value 1630 and a total "bad" predictive value 1640 being associated with each ad/query feature 1610. Each predictive value determined in block 1405 can be summed with a current value stored in entries 1630 or 1640 that corresponds to each ad/query feature 1610 that is further associated with the advertisement and query at issue. As an example, assume that an ad for "800flowers.com" is provided to a user in response to the search query "flowers for mother's day." The session features associated with the selection of the ad return a probability P(good ad | ad selection) of 0.9.
Article titled “ Marketing Spot Optimization” teaches The measurement engine 210 may be used to evaluate actual spot impressions relative to predicted impressions. The measurement engine 210 also may receive inputs from analytics services such as those that record and analyze viewing information based on panels of viewers, or other selected audience segments. The audience valuation and optimizer engine 220 may estimate the value of upcoming spot inventory, such as for a day, or a week. The valuation may be based on historical data. The audience forecasting engine 230 estimates the potential audience composition (e.g., demographics) for one spot or a group of spots (e.g., by day part). The yield optimization engine 240 provides an estimate of return on investment for spots allocated to self-marketing or to ad sales.
In operation, the optimization program 200 may start with a given set of spot inventory, such as an entire day's worth of upcoming spots, or an entire week's worth of upcoming spots, and then estimate the value of each spot if used for self-marketing purposes. This valuation could be in units such as "cost per call" or "cost per attributed visit." The valuation may be based on historical values. The value may be expressed in the form of a response rate such as 0.2% of impressions will generate a response, also may be expressed by date, time, network, and ad creative.
Point of contact
Any inquiry concerning this communication or earlier communications from the examiner should be directed to RAQUEL ALVAREZ whose telephone number is (571)272-6715. The examiner can normally be reached Mondays thru Thursdays 8:30-6:30.
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If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, Ilana Spar can be reached at 571-270-7537. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
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/RAQUEL ALVAREZ/ Primary Examiner, Art Unit 3622
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