DETAILED ACTION
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 § 112
The following is a quotation of the first paragraph of 35 U.S.C. 112(a):
(a) IN GENERAL.—The specification shall contain a written description of the invention, and of the manner and process of making and using it, in such full, clear, concise, and exact terms as to enable any person skilled in the art to which it pertains, or with which it is most nearly connected, to make and use the same, and shall set forth the best mode contemplated by the inventor or joint inventor of carrying out the invention.
The following is a quotation of the first paragraph of pre-AIA 35 U.S.C. 112:
The specification shall contain a written description of the invention, and of the manner and process of making and using it, in such full, clear, concise, and exact terms as to enable any person skilled in the art to which it pertains, or with which it is most nearly connected, to make and use the same, and shall set forth the best mode contemplated by the inventor of carrying out his invention.
Claim 21 -40 are rejected under 35 U.S.C. 112(a) or 35 U.S.C. 112 (pre-AIA ), first paragraph, as failing to comply with the enablement requirement. The claim(s) contains subject matter which was not described in the specification in such a way as to enable one skilled in the art to which it pertains, or with which it is most nearly connected, to make and/or use the invention. The independent claims 21, 28 and 35 recite the limitation of “providing, by the one or more processors, one or more parameters, the historical data, the tracking data, and the proposed bet selection, to an artificial intelligence model trained to identify associations between the one or more parameters, the historical data, the tracking data, and the proposed bet selection and output a plurality of insights associated with the proposed bet selection; and” (claim 21 is an example claim, as claims 28 and 35 recite almost identical limitations).
The Examiner notes that paragraph 0038 provides the only reference or description of using artificial intelligence models and states “Bet selection handler 122 may be configured to generate the proposed bet selection based on data retrieved from data store 118. For example, bet selection handler 122 may utilize one or more artificial intelligence models to analyze the data and generate a proposed bet selection in accordance with the constraints set by the user.” The Examiner notes that the specification generally teaches using an artificial intelligence model to analyze data from a data store to generate a proposed bet selection according to the constraints set by the user. When looking at claim 21, the claim provides a totally different use of artificial intelligence wherein an artificial intelligence model is trained to identify associations between the one or more parameters, the historical data, the tracking data, and the proposed bet selection and output a plurality of insights associated with the proposed bet selection. The Examiner notes that the specification fails to enable one of ordinary skill in the art to use the entire scope of the claimed artificial intelligence without undue experimentation.
The Examiner looks to the Wands factors to support the above conclusion.
(A) The breadth of the claims:
The scope of claim 21 broadly encompasses the usage of an artificial intelligence model that is trained to identify associations between four different specific categories of data and generate insights based on them. The claims does not specify the manner of training, how the inputs are represented or the way or means with which the associations are converted into insights. When looking at the specification, the specification does not provide any embodiment or manner of the artificial intelligence performing this functionality, but rather paragraph 0038 discloses using artificial intelligence for a different function, that of generating a proposed bet selection. Thus as can be clearly seen, the scope of claims 21, 28 and 35 are much broader that the implementation of artificial intelligence found in the specification.
(F) The amount of direction provided by the inventor:
The Examiner notes that the specification does not provide any direction for carrying out the specific artificial intelligence method of claim 21. Claim 21 fails to detail how the parameters, historical data, tracking data and proposed best selection are provided to the artificial intelligence model. It does not identify what the associations actually are between the different types of data, or how the insights are identified by the AI model or how the associations are used to generate the insights. Thus one or ordinary skill Is not provided with enough direction as to how the claims functionality with respect to artificial intelligence is accomplished.
(H) The quantity of experimentation needed to make or use the invention based on the content of the disclosure:
The specification fails to provide guidance as detailed above and thus to operate claim 21, 28 and 35, it would require more than routine programming. A person would need to determine how the claimed types of data are acquired and represented, how they are input into the artificial intelligence model, and how the associations are translated or converted into a plurality of insights. The specification fails to provide examples or embodiments of these operations. Thus as can be seen, the claimed method or processes require more than routine implementation of artificial intelligence to make these determinations.
After considering at least these factors the claims (claims 21 – 40) fail to comply with the enablement requirement.
Claims 21 – 40 are rejected under 35 U.S.C. 112(a) or 35 U.S.C. 112 (pre-AIA ), first paragraph, as failing to comply with the written description requirement. The claim(s) contains subject matter which was not described in the specification in such a way as to reasonably convey to one skilled in the relevant art that the inventor or a joint inventor, or for applications subject to pre-AIA 35 U.S.C. 112, the inventor(s), at the time the application was filed, had possession of the claimed invention. Claims 21, 28 and 35 require “providing, by the one or more processors, one or more parameters, the historical data, the tracking data, and the proposed bet selection, to an artificial intelligence model trained to identify associations between the one or more parameters, the historical data, the tracking data, and the proposed bet selection and output a plurality of insights associated with the proposed bet selection;”. The specification describes the use of parameters, the use of historical data, the use of tracking data, the use of bet selections and outputting insights, but fails to describe or detail the combination of these elements in conjunction with an artificial intelligence model. The specification at paragraph 0038 describes using AI to analyze data and generate a proposed bet selection, but fails to describe inputting a proposed bet selection into an AI model that identifies associations between the various pieces of data and outputs insights based upon them.
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.
This subject matter eligibility analysis follows the latest guidance for Patent Subject Matter Eligibility Guidance.
Claims 21 - 40 are rejected under 35 U.S.C. 101 because the claimed invention is directed to non-statutory subject matter.
Step 1:
Initially, under Step 1 of the analysis, it is noted that the claims are directed towards eligible categories of subject matter.
Step 2A:
Prong 1: Does the Claim recite an Abstract idea, Law of Nature, or Natural Phenomenon?
Claims 21 - 34 are exemplary because they require substantially the same operative limitations of the remaining claims (reproduced below.) Examiner has underlined the claim limitations which recite the abstract idea, discussed in detail in the paragraphs that follow.
21. (New) A computer-implemented method for generating insights using an organization computing system, the method comprising:
receiving, by one or more processors, a proposed bet selection for an event from a user interface, wherein the proposed bet selection comprises team information and opponent information;
generating, by the one or more processors, a plurality of queries for a data store by analyzing the proposed bet selection;
retrieving, by the one or more processors and from the data store, historical data related to the proposed bet selection according to the plurality of queries;
receiving, by the one or more processors, a tracking feed for the event, wherein the tracking feed comprises at least one of a video feed and a tracking system output;
generating, by the one or more processors, tracking data for the event using the tracking feed;
providing, by the one or more processors, one or more parameters, the historical data, the tracking data, and the proposed bet selection, to an artificial intelligence model trained to identify associations between the one or more parameters, the historical data, the tracking data, and the proposed bet selection and output a plurality of insights associated with the proposed bet selection; and
providing, by the one or more processors, the plurality of insights to the user interface.
The claims recite italicized limitations that fall within at least one of the groupings of abstract ideas enumerated in the 2019 PEG, namely, Mental Process
More specifically, under this grouping, the italicized limitations represent concepts performed in the human mind (including an observation, evaluation, judgment, opinion). For example, the italicized limitations are directed towards the receiving of a proposed wager, analyzing the proposed wager, retrieving historical data regarding the wager, observing a tracking feed comprising a video feed, generating tracking data and providing insights to be processed and analyzed to determine associations between the data to thereby provide insights to be presented to a user.
Prong 2: Does the Claim recite additional elements that integrate the exception into a practical application of the exception?
Although the claims recite additional limitations, these limitations do not integrate the exception into a practical application of the exception. For example, the claims require additional limitations as follow, (emphasis added): processors, user interfaces, and artificial intelligence.
These additional limitations do not represent an improvement to the functioning of a computer, or to any other technology or technical field, (MPEP 2106.05(a)). Nor do they apply the exception using a particular machine, (MPEP 2106.05(b)). Furthermore, they do not effect a transformation. (MPEP 2106.05(c)). Rather, these additional limitations amount to an instruction to “apply” the judicial exception using a computer as a tool to perform the abstract idea. Therefore, since the additional limitations, individually or in combination, are indistinguishable from a computer used as a tool to perform the abstract idea, the analysis continues to Step 2B, below.
Step 2B:
Under Step 2B, the claims do not include additional elements that are sufficient to amount to significantly more than the judicial exception because they amount to conventional and routine computer implementation and mere instructions for implementing the abstract idea on generic computing devices.
For example, as pointed out above, the claimed invention recites additional elements facilitating implementation of the abstract idea. Applicant has claimed computer processors, user interfaces, and artificial intelligence. However, all of these elements viewed individually and as a whole, are indistinguishable from conventional computing elements known in the art. Therefore, the additional elements fail to supply additional elements that yield significantly more than the underlying abstract idea.
As the Alice court cautioned, citing Flook, patent eligibility cannot depend simply on the draftsman’s art. Here, amending the claims with generic computing elements does not (in this Examiner’s opinion), confer eligibility.
Regarding the Berkheimer decision, Applicants specification establishes that these additional elements are generic:
[0038] In some embodiments, bet selection handler 122 may be configured to optimize or improve a proposed bet selection. For example, a user may provide bet selection handler 122 with one or more of an event, a number of legs, and a risk level (e.g., scale of 1-10 from least risky to most risky) so that bet selection handler 122 can build a proposed bet selection for the user. Bet selection handler 122 may be configured to generate the proposed bet selection based on data retrieved from data store 118. For example, bet selection handler 122 may utilize one or more artificial intelligence models to analyze the data and generate a proposed bet selection in accordance with the constraints set by the user.
[0081] Figure 6A illustrates an architecture of computing system 600, according to example embodiments. System 600 may be representative of at least a portion of organization computing system 104. One or more components of system 600 may be in electrical communication with each other using a bus 605. System 600 may include a processing unit (CPU or processor) 610 and a system bus 605 that couples various system components including the system memory 615, such as read only memory (ROM) 620 and random access memory (RAM) 625, to processor 610. System 600 may include a cache of high-speed memory connected directly with, in close proximity to, or integrated as part of processor 610. System 600 may copy data from memory 615 and/or storage device 630 to cache 612 for quick access by processor 610. In this way, cache 612 may provide a performance boost that avoids processor 610 delays while waiting for data. These and other modules may control or be configured to control processor 610 to perform various actions. Other system memory 615 may be available for use as well. Memory 615 may include multiple different types of memory with different performance characteristics. Processor 610 may include any general purpose processor and a hardware module or software module, such as service 1 632, service 2 634, and service 3 636 stored in storage device 630, configured to control processor 610 as well as a special-purpose processor where software instructions are incorporated into the actual processor design. Processor 610 may essentially be a completely self-contained computing system, containing multiple cores or processors, a bus, memory controller, cache, etc. A multi-core processor may be symmetric or asymmetric.
[0088] While the foregoing is directed to embodiments described herein, other and further embodiments may be devised without departing from the basic scope thereof. For example, aspects of the present disclosure may be implemented in hardware or software or a combination of hardware and software. One embodiment described herein may be implemented as a program product for use with a computer system. The program(s) of the program product define functions of the embodiments (including the methods described herein) and can be contained on a variety of computer-readable storage media. Illustrative computer-readable storage media include, but are not limited to: (i) non-writable storage media (e.g., read-only memory (ROM) devices within a computer, such as CD-ROM disks readably by a CD-ROM drive, flash memory, ROM chips, or any type of solid-state non-volatile memory) on which information is permanently stored; and (ii) writable storage media (e.g., floppy disks within a diskette drive or hard-disk drive or any type of solid state random-access memory) on which alterable information is stored. Such computer-readable storage media, when carrying computer-readable instructions that direct the functions of the disclosed embodiments, are embodiments of the present disclosure.
Regarding the Berkheimer decision, Guaffin (US 2021/0026657) establishes that these additional elements are generic:
[0065] Optionally, the first digital entity associated with the first user is an artificial intelligence bot. The artificial intelligence bot refers to an autonomous program trained through the artificial intelligence, present on a network such as the cloud server or the user device that interacts with the plurality of applications in the application portal. Herein, in the present disclosure, the artificial intelligence bot is configured to behave in a manner that is similar to a user playing games on the gaming applications. For example, the artificial intelligence bot mimics the behavior of the user, such as actions performed by the user to clear levels in the application such as a gaming application. The term “artificial intelligence” as used herein relates to any mechanism or computationally intelligent system that combines knowledge, techniques, and methodologies for controlling a bot, such as the first digital entity or other element within a computing environment. Furthermore, the artificial intelligence (AI) is configured to apply knowledge and that can adapt it-self and learn to do better in changing environments. Additionally, employing any computationally intelligent technique, the artificial intelligence (AI) is operable to adapt to unknown or changing environment for better performance. The artificial intelligence (AI) includes fuzzy logic engines, decision-making engines, pre-set targeting accuracy levels, and/or programmatically intelligent software. The artificial intelligence (AI) in the context of the present disclosure relates to software-based algorithms that are executable upon computing hardware and are operable to adapt and adjust their operating parameters in an adaptive manner depending upon information that is presented to the software-based algorithms when executed upon the computing hardware. Optionally, artificial intelligence (AI) employ any one or combination of the following computational techniques: constraint program, fuzzy logic, classification, conventional artificial intelligence, symbolic manipulation, fuzzy set theory, evolutionary computation, cybernetics, data mining, approximate reasoning, derivative-free optimization, decision trees, or soft computing.
Therefore, these elements fail to supply additional elements that yield significantly more than the underlying abstract idea. Thus, taken alone, the additional elements do not amount to significantly more than the above-identified judicial exception (the abstract idea).
Looking at the limitations as an ordered combination adds nothing that is not already present when looking at the elements taken individually. There is no indication that the combination of elements improves the functioning of a computer or improves any other technology. Their collective functions merely provide conventional computer implementation.
Moreover, the claims do not recite improvements to another technology or technical field. Nor, do the claims improve the functioning of the underlying computer itself -- they merely recite generic computing elements. Furthermore, they do not effect a transformation of a particular article to a different state or thing: the underlying computing elements remain the same.
Concerning preemption, the Federal Circuit has said in Ariosa Diagnostics, Inc., V. Sequenom, Inc., (Fed Cir. June 12, 2015):
The Supreme Court has made clear that the principle of preemption is the basis for the judicial exceptions to patentability. Alice, 134 S. Ct at 2354 (“We have described the concern that drives this exclusionary principal as one of pre-emption”). For this reason, questions on preemption are inherent in and resolved by the § 101 analysis. The concern is that “patent law not inhibit further discovery by improperly tying up the future use of these building blocks of human ingenuity.” Id. (internal quotations omitted). In other words, patent claims should not prevent the use of the basic building blocks of technology—abstract ideas, naturally occurring phenomena, and natural laws. While preemption may signal patent ineligible subject matter, the absence of complete preemption does not demonstrate patent eligibility. In this case, Sequenom’s attempt to limit the breadth of the claims by showing alternative uses of cffDNA outside of the scope of the claims does not change the conclusion that the claims are directed to patent ineligible subject matter. Where a patent’s claims are deemed only to disclose patent ineligible subject matter under the Mayo framework, as they are in this case, preemption concerns are fully addressed and made moot. (Emphasis added.)
For these reasons, it appears that the claims are not patent-eligible under 35 USC §101.
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.
Claim(s) 21 - 40 is/are rejected under 35 U.S.C. 103 as being unpatentable over Lewski (US 2017/0140605) in view of Schwartz et al (US 2020/0234543) in view of Sha et al (US 2020/0279115).
As per claim 21, Lewski discloses:
receiving, by one or more processors, a proposed bet selection for an event from a user interface, wherein the proposed bet selection comprises team information and opponent information; (Lewski a computing system comprising a plurality of processors and further discloses receiving a proposed bet selection for a game event comprising team information and opponent information) (Lewski 0015, 0016, 0018 – 0022, 0091-0100)
generating, by the one or more processors, a plurality of queries for a data store by analyzing the proposed bet selection; ( Lewski discloses generating queries to store the bet in a betting database (0024 – 0025) and analyzation of the bet to determine odds for the bet) (Lewski 0026)
retrieving, by the one or more processors and from the data store, historical data related to the proposed bet selection according to the plurality of queries; (Lewski discloses the retrieving of historical data from a historical data set corresponding to the bet selection queries) (Lewski 0026 – 0028)
providing, by the one or more processors, one or more parameters, the historical data, …, and the proposed bet selection, …associations between the one or more parameters, the historical data, the …, and the proposed bet selection and output a plurality of insights associated with the proposed bet selection; and providing, by the one or more processors, the plurality of insights to the user interface. (Lewski discloses generation and displaying of insights such as updated odds for a proposed bet based upon correlations or associations between the bet selection, the statistics of the subjects of the bet and historical data and displaying the odds to the user for acceptance) (Lewski 0026 – 0028)
Lewski fails to disclose specifically:
receiving, by the one or more processors, a tracking feed for the event, wherein the tracking feed comprises at least one of a video feed and a tracking system output;
generating, by the one or more processors, tracking data for the event using the tracking feed;
…the tracking data…
…to an artificial intelligence model trained to identify…
In a similar field of endeavor, Shwartz discloses the use of AI that is used to generate a plurality of insights between situational data (tracking data such as video camera feeds) , historical data and current game situations as they relate to potential wagering events (Schwartz 0018, 0022, 0032 – 0036, 0137, 0138, 0164, 0164)
It would be obvious to one of ordinary skill in the art, at the time of filing, to modify Lewski in view of Schwartz to use a known technique to improve similar devices in the same way, by utilizing AI to generate insights that are displayed to a user, wherein the insights are based upon correlations between tracking data, historical data and current game data. That is, the use of AI would improve the speed at which correlations can be quickly identified and presented to a user.
However, in a similar field of endeavor, Sha teaches a system that utilizes a broadcasted video of a game to generate player tracking data (Sha 0030 – 0034, 0037).
It would be obvious to one of ordinary skill in the art, at the time of filing, to modify Lewski in view of Sha to use a known technique to improve similar devices in the same way by means of utilizing a broadcast video of an event such as a sports game to generate tracking data that can be further analyzed. This would be beneficial as the system would be able to have a more complete unified view of the game to thereby ensure that the system has the most complete information to thereby offer accurate insights to a user with respect to their proposed wager (Sha 0033).
As per claim 22, generating, by the one or more processors, a suggested wager based on the historical data and the proposed bet selection; and providing, by the one or more processors, the suggested wager to the user interface. (Lewski discloses the provision of the updated wager odds to the player for acceptance) (Lewski 0028)
As per claim 23, generating, by the one or more processors, a plurality of user queries based on the plurality of queries for the data store and the proposed bet selection; and providing, by the one or more processors, the plurality of user queries to the user interface. (Lewski discloses the option to modify wagers in the event that the odds are not acceptable and to thereby generate further proposed wagers (i.e. queries) that are presented on the interface) (Lewski 0029)
As per claim 24, wherein the plurality of user queries are associated with one or more of the team information and the opponent information. (Lewski 0029)
As per claim 25, receiving, by the one or more processors, an indication that the proposed bet selection was converted to an actual wager; providing, by the one or more processors, the actual wager and real-time event data associated with the event, to the artificial intelligence model trained to identify associations between the actual wager and the real-time event data and output a plurality of additional insights associated with the actual wager; and providing, by the one or more processors, the plurality of additional insights to the user interface. (Combination of Lewski in view of Schwartz wherein Shwartz teaches the use of AI to determine insights such as updated odds for a wager that can be made in real time as the game progresses) (Schwartz 0036, 0039)
As per claim 26, generating, by the one or more processors, a visual recreation of the historical data, wherein the visual recreation is generated based on one or more of the historical data, the plurality of insights, and the tracking data; and providing, by the one or more processors, the visual recreation to the user interface. (Lewski discloses the visual recreation of tracking data such as player positioning) (Lewski 0163)
As per claim 27, wherein the proposed bet selection further comprises odds information. (Lewski 0026-0028, 0161)
Independent claim(s) 28 and 35 is/are made obvious by the combination of Lewski, Schwartz and Sha based on the same analysis set forth for claim(s) 1, which are similar in claim scope.
Dependent claim(s) 29-34 is/are made obvious by the combination of Lewski, Schwartz and Sha based on the same analysis set forth for claim(s) 22-27, which are similar in claim scope.
Dependent claim(s) 36-40 is/are made obvious by the combination of Lewski, Schwartz and Sha based on the same analysis set forth for claim(s) 22-26, which are similar in claim scope.
Conclusion
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/RAW/Examiner, Art Unit 3715 9/25/2026
/KANG HU/Supervisory Patent Examiner, Art Unit 3715
