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 .
Acknowledgments
Claims 1-20 are pending.
This Application is a continuation application of Application 17566471.
Applicant did not provide information disclosure statement.
Continuation
This application is a continuation application of U.S. application no. 17566471 filed on December 30, 2021 now U.S. Patent 12277568 (“Parent Application”). See MPEP§201.07. In accordance with MPEP §609.02 A. 2 and MPEP §2001.06(b) (last
paragraph), the Examiner has reviewed and considered the prior art cited in the Parent
Application. Also in accordance with MPEP §2001.06(b) (last paragraph), all documents
cited or considered ‘of record’ in the Parent Application are now considered cited or ‘of
record’ in this application. Additionally, Applicant(s) are reminded that a listing of the
information cited or ‘of record’ in the Parent Application need not be resubmitted in this
application unless Applicants desire the information to be printed on a patent issuing from this application. See MPEP §609.02 A. 2. Finally, Applicants are reminded that the
prosecution history of the Parent Application is relevant in this application. See e.g.,
Microsoft Corp. v. Multi-Tech Sys., Inc., 357 F.3d 1340, 1350, 69 USPQ2d 1815, 1823
(Fed. Cir. 2004) (holding that statements made in prosecution of one patent are relevant to the scope of all sibling patents).
Allowable Subject Matter
Claims 3, 10, and 17 are allowable if rewritten to include all of the limitations of the base claim and any intervening claims, and if the independent claims were amended in such a way as to overcome the rejection(s) under 35 U.S.C. 101, set forth in this Office action. The closest prior art to these claims include Soon-Shiong (US20150039443A1) in further view of Price (US20210350307A1) in further view of Kannan (US20160342911A1) in further view of Rowley (US20180053264A1) in further view of Boss (US20180341898A1) who teaches test data. However, with respect to exemplary claim 3, 10, and 17, the closest prior art of record, either alone or taken in combination with any other references of record, do not anticipate or render obvious the claimed functionality of claim 3, 10, and 17.
Claims 6, 13, and 19 are allowable if rewritten to include all of the limitations of the base claim and any intervening claims, and if the independent claims were amended in such a way as to overcome the rejection(s) under 35 U.S.C. 101, set forth in this Office action. The closest prior art to these claims include Soon-Shiong (US20150039443A1) in further view of Price (US20210350307A1) in further view of Kannan (US20160342911A1) in further view of Rowley (US20180053264A1) in further view of Mourad (US20060080210A1) who teaches distance from dealers to customers. However, with respect to exemplary claim 6, 13, and 19, the closest prior art of record, either alone or taken in combination with any other references of record, do not anticipate or render obvious the claimed functionality of claim 6, 13, and 19.
Claim Rejections - 35 USC § 101
35 U.S.C. 101 reads as follows:
Whoever invents or discovers any new and useful process, machine, manufacture, or composition of matter, or any new and useful improvement thereof, may obtain a patent therefor, subject to the conditions and requirements of this title.
Claims 1-20 are rejected under 35 U.S.C. 101 because the claimed invention is directed to a judicial exception (i.e., a law of nature, a natural phenomenon, or an abstract idea) without significantly more than the judicial exception itself.
Regarding Step 1 of subject matter eligibility for whether the claims fall within a statutory category (See MPEP 2106.03), claims 1-20 are directed to computer program product comprising a non-transitory computer-readable medium, system, and method.
Regarding step 2A-1, Claims 1-18 recite a Judicial Exception. Exemplary independent claim 1 and similarly claims 8 and 15 recite the limitations of
Preparing…datasets using clickstream data associated with visitors… any user information that the visitors… and information about dealers… the clickstream data including clickstreams collected…as the visitors browse… the datasets thus prepared containing user-related features associated with lead submissions, dealer-related features associated with the dealers, and vehicle-related features associated with vehicles, each respective lead submission associated with a user of the website who becomes a lead through the respective lead submission… training… model using the datasets until the…model meets a performance criterion, wherein the training includes, for each lead submission associated with a user of interest, taking a user-related feature associated with the user of interest, a dealer-related feature associated with a dealer of interest, and a vehicle-related feature associated with a vehicle of interest as input and, based on the input, generating a user value for the user of interest, wherein the user value indicates how likely the user of interest is to make a purchase of the vehicle of interest from the dealer…generating, by the…model thus trained, user values… from a past time window; determining… utilizing the user values, a corresponding user lifetime value for each of the users… the determining comprising multiplying each of the user values as a weight with a predetermined value; and…
These limitations, as drafted, are a process that, under its broadest reasonable interpretation cover concepts of preparing, training, generating, and determining data. The claim limitations fall under the abstract idea grouping of mental process, because the limitations can be performed in the human mind, or by a human using a pen and paper. For example, but for the language of a system and non-transitory computer-readable medium, the claim language encompasses simply preparing datasets, training a model, generating a user value, and determining a user lifetime value. The claims also recite the mathematical concept of multiplication. These are mere data manipulation steps that do not require a computer. A user is able to determine user values by preparing datasets and training a model. The claimed invention is merely automating a manual process.
The claims also recite users who are consumers and making a purchase of a vehicle. The claims also recite user lifetime values with respect to a business. This clearly teaches user management such as looking at their behaviors with respect to clickstream information. These make the claims fall in the abstract idea grouping of certain methods of organizing human activity (sales activity, fundamental economic principles or practices; business relations, interactions between people). It is clear the limitations recite these abstract idea groupings, but for the recitations of generic computer components. The mere nominal recitations of generic computer components does not take the limitations out of the mental process, mathematical concepts, and certain methods of organizing human activity grouping. The claims are focused on the combination of these abstract idea processes.
Regarding step 2A-2- This judicial exception is not integrated into a practical application, and the claims do not include additional elements that are sufficient to amount to significantly more than the judicial exception.
The independent claim recites the additional elements of machine learning model, website, vehicle data system, processor, computer program product, a non-transitory computer-readable medium, and communicating user lifetime values corresponding to the users of the website to a search engine…
These components are recited at a high level of generality, and merely automate the steps. Each of the additional limitations is no more than mere instructions to apply the exception using a generic computer component.
The combination of these additional elements is no more than mere instructions to apply the exception using a generic computer components or software. Accordingly, even in combination, these additional elements do not integrate the abstract idea into a practical application because they do not impose any meaningful limits on practicing the abstract idea.
Further, the claims do not provide for recite any improvements to the functioning of a computer, or to any other technology or technical field; applying or using a judicial exception to effect a particular treatment or prophylaxis for a disease or medical condition; applying the judicial exception with, or by use of, a particular machine; effecting a transformation or reduction of a particular article to a different state or thing; or applying or using the judicial exception in some other meaningful way beyond generally linking the use of the judicial exception to a particular technological environment, such that the claim as a whole is more than a drafting effort designed to monopolize the exception.
The dependent claims have the same deficiencies as their parent claims as being directed towards an abstract idea, as the dependent claims merely narrow the scope of their parent claims. For example, the dependent claims further describe additional details about the data seen in the independent claims such as dealer/vehicle type. In addition, the dependent claims further recite details of the additional elements such as the website.
Regarding step 2B the claims do not include additional elements that are sufficient to amount to significantly more than the judicial exception because claim 1 recites
Method, however method is not considered an additional element
Claim 1 further recites vehicle data system, website, process, non- transitory computer-readable medium, machine learning model
Claims 4 and 11 recite emails.
Claim 8 recites vehicle data system, processor, non- transitory computer-readable medium, machine learning model, website
Claim 15 recites computer program product comprising a non-transitory computer-readable medium, processor, vehicle data system, website, machine learning model.
Claims 1, 8, and 15 recite and communicating user lifetime values corresponding to the users of the website to a search engine
When looking at these additional elements individually, the additional elements are purely functional and generic the Applicant specification states a general purpose computer in para 0076.
When looking at the additional elements in combination, the Applicant’s specification merely states a general purpose computer as seen in para 0076. The computer components add nothing that is not already present when the steps are considered separately. See MPEP 2106.05
Looking at these limitations as an ordered combination and individually adds nothing additional that is sufficient to amount to significantly more than the recited abstract idea because they simply provide instructions to use generic computer components, recitations of generic computer structure to perform generic computer functions that are used to "apply" the recited abstract idea. Thus, the elements of the claims, considered both individually and as an ordered combination, are not sufficient to ensure that the claim as a whole amounts to significantly more than the abstract idea itself.
Since there are no limitations in these claims that transform the exception into a patent eligible application such that these claims amount to significantly more than the exception itself, claims 1-20 are rejected under 35 U.S.C. 101.
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.
Claim(s) 1, 2, 4, 5, 7, 8, 9, 11, 12, 14, 15, 16, 18, and 20 is/are rejected under 35 U.S.C. 103 as being unpatentable over Soon-Shiong (US20150039443A1) in further view of Price (US20210350307A1) in further view of Kannan (US20160342911A1) in further view of Rowley (US20180053264A1)
Regarding claims 1 and similarly claims 8 and 15, Soon-Shiong teaches
A method (See para 0010- The inventive subject matter provides apparatus, systems and methods in which one can manage a consumer's engagement points to provide personalized content to the consumer as the consumer flows through an expected behavior pattern) This shows a method.
A vehicle data system, comprising: a processor; a non-transitory computer-readable medium; and instructions stored on the non-transitory computer-readable medium and translatable by the processor for (See figure 1) This shows a vehicle data system such as engagement engine 130 and database 160. This is a vehicle data system since the system deals with vehicle data as seen here (See para 0037- The engagement point database 160 stores and manages engagement points as distinct objects. The engagement point database 160 stores engagement points as individual engagement points, or as a pre-arranged group of engagement points. For example, the engagement point database 160 can store engagement points individually that reflect “interest in purchasing a new vehicle”, “searching a new model of vehicle”, “interest in obtaining a car loan”, or other types of engagement points. ) The engagement system includes processor and memory as seen here (See para 0021- One should appreciate the computing devices comprise a processor configured to execute software instructions stored on a tangible, non-transitory computer readable storage medium (e.g., hard drive, solid state drive, RAM, flash, ROM, etc.).) (See para 0023- Both engagement point database 160 and engagement engine 130 are computing devices having software instructions stored in their respective non-transitory, computer readable memories that cause their respective hardware processor to execute the roles or responsibilities discussed below. )
A computer program product comprising a non-transitory computer-readable medium storing instructions translatable by a processor of a vehicle data system for (See para 0021- One should appreciate the computing devices comprise a processor configured to execute software instructions stored on a tangible, non-transitory computer readable storage medium (e.g., hard drive, solid state drive, RAM, flash, ROM, etc.).) (See para 0023- Both engagement point database 160 and engagement engine 130 are computing devices having software instructions stored in their respective non-transitory, computer readable memories that cause their respective hardware processor to execute the roles or responsibilities discussed below. ) This shows memory.
preparing, by a vehicle data system, datasets using clickstream data associated with visitors of the website, any user information that the visitors provided through the website, and information about dealers affiliated with the website…the clickstream data including clickstreams collected by the website as the visitors browse through the website, (See para 0024- Consumer behavior data can comprise digital representations of images, sounds, smells, tastes, touch, biometrics, or other data modalities that can be sensed or represented as digital data) (See para 0026- In another example, based on a click-stream history of the consumer that shows frequent visits to a vehicle manufacturer website or a dealer website, the engagement engine can derive a context that represents the consumer can be interested in purchasing a vehicle, obtaining a car loan with a low interest rate, receiving ongoing promotions, seeking maintenance, or otherwise being receptive to third party engagement.) This shows datasets are prepared from consumer behavior data for the system to process. The behavior data includes clickstream information with respect to dealers and vehicle websites. The clickstream data would include information on what dealer website the user visited. The click stream history would also include any information the user provided to the website such as what they searched for or what pages and links they pressed on the website.
the datasets thus prepared containing user-related features associated with lead submissions, dealer-related features associated with the dealers, and vehicle-related features associated with vehicles The clickstream datasets for the consumers contain user related features (i.e. where the user went on the websites) associated with the context of being a lead submission (i.e. buying a car, getting a loan, buying maintenance) (See para 0026- the consumer can be interested in purchasing a vehicle, obtaining a car loan with a low interest rate, receiving ongoing promotions, seeking maintenance, or otherwise being receptive to third party engagement.) The clickstream information would also have vehicle related features and dealer related features because the clickstream information would have the different dealers and vehicles the consumer looked up.
each respective lead submission associated with a user of the website who becomes a lead through the respective lead submission The user becomes a lead because the system determines a context with respect to the clickstream dataset of the user (i.e. buying a car, getting a loan, buying maintenance). Determining a context is seen in fig. 3 item 340.
the vehicle data system including a processor and a non- transitory computer-readable medium The engagement system includes processor and memory as seen here (See para 0021- One should appreciate the computing devices comprise a processor configured to execute software instructions stored on a tangible, non-transitory computer readable storage medium (e.g., hard drive, solid state drive, RAM, flash, ROM, etc.).) (See para 0023- Both engagement point database 160 and engagement engine 130 are computing devices having software instructions stored in their respective non-transitory, computer readable memories that cause their respective hardware processor to execute the roles or responsibilities discussed below. )
Even though Soon-Shiong teaches vehicle data system, lead submissions, and click stream datasets for consumers, it doesn’t teach machine learning model and training, however Price teaches
training, by the vehicle data system, a machine learning model using the datasets until the machine learning model meets a performance criterion (See para 0047- For example, computing device 101 may allow machine learning software 127 to train a model to reach a threshold accuracy (e.g., 80%, 90%, 95%, etc.), and then prevent machine learning software 127 from training or improving the model further.) This teaches training a machine learning model until a performance criterion is met.
Price further expands on the training as seen here wherein the training includes, for each lead submission associated with a user of interest, taking a user-related feature associated with the user of interest, a dealer-related feature associated with a dealer of interest, and a vehicle-related feature associated with a vehicle of interest as input and, based on the input, generating a user value for the user of interest (See para 0046- Machine learning software 127 may take as input customer, vehicle, and dealership information and may output a value. The value may indicate a maximum loan amount and/or the likelihood that the customer will make a purchase. ) This shows that user information, dealer information, and vehicle information are used as input and an output is generated. The output contains user value such as maximum loan amount for the consumer and/or likelihood of making a purchase.
wherein the user value indicates how likely the user of interest is to make a purchase of the vehicle of interest from the dealer of interest through the website (See para 0046- Machine learning software 127 may take as input customer, vehicle, and dealership information and may output a value. The value may indicate a maximum loan amount and/or the likelihood that the customer will make a purchase. ) The output contains user value such as maximum loan amount for the consumer and/or likelihood of making a purchase. Price teaches vehicle shopping website to make purchases as seen here (See para 0035- Customer information may include any data corresponding to how the customer uses a vehicle shopping website.)
generating, by the vehicle data system utilizing the machine learning model thus trained, user values of users of the website from a past time window; The user values of likelihood to buy a vehicle deal is with historical data (i.e. past time window) since this data is received first from the user and then a value is generated. This generated user values can be done with a trained machine learning after it reaches accuracy as seen in para 0047.
Soon-Shiong and Price are analogous art because they are from the same problem solving area of analyzing consumers with respect to making a purchase and both belong to G06Q30 classification. It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to have combined Soon-Shiong’s invention by incorporating the method of Price because Soon-Shiong would also be able to use machine learning to determine consumer expected behavior patterns and linking them to engagement points. The machine learning model would become more accurate over time and the right engagement points would be linked to the correct behavior patterns. Using machine learning models would also make the system of Price more sophisticated since machine learning is a complex process that is able to handle larger data sets and more complex problems.
Even though Soon-Shiong teaches consumers and Price teaches user values with respect to making a purchase, they do not teach user lifetime values, however Kannan teaches
determining, by the vehicle data system utilizing the user values, a corresponding user lifetime value for each of the users of the website, the determining comprising multiplying each of the user values as a weight with a predetermined value; (See para 0089- In an illustrative example, the initial estimate of customer value may be determined in form a Customer Lifetime Value (CLV) estimate. ) This teaches a lifetime value. This is with respect to users on a website making a transaction (See para 0026- the customer is currently viewing on an enterprise website.) Determining lifetime value includes multiplying data as seen here (See para 0059- (for example, a corrected CLV estimate). For example, if the aggregate persona type is associated with a pre-determined correction factor of ‘0.85’ and if the initial estimate of the customer value is 1000 US dollars, then the corrected estimate of the customer value may be determined, in one example embodiment, by simply multiplying the pre-determined correction factor with the initial estimate of the customer value, i.e. 0.85×1000, to generate the corrected estimate of customer value of 850 US dollars.)
Soon-Shiong, Price, and Kannan are analogous art because they are from the same problem solving area of analyzing consumers with respect to making a purchase and all belong to G06Q30 classification. It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to have combined Soon-Shiong’s and Price’s invention by incorporating the method of Kannan because Soon-Shiong would also be able to use lifetime values for the consumers when determining what kind of engagement points to use with the consumer. This makes the system of Soon-Shiong more sophisticated since it would provide more in-depth data about consumers. Price would also be able to use lifetime values for the customers when determining recommendations which kind of sales associate to deal with the customers. Picking the right sales associate would translate to successful sales. This makes the system of Price more sophisticated since it would provide more in-depth data about consumers.
Even though Kannan teaches lifetime value, it is unclear if this data is given to a search engine, however Rowley teaches
And communicating, by the vehicle data system to a search engine, user lifetime values corresponding to the users of the website (See para 0042- The engine then loads the consumer's lifetime value and event history. Based on each member's status and history, the club engine creates a personalized rules set and configures programs, discounts, price points, virtual currencies, payment options, and the like.) (See para 0082- The prioritization module refers to the order in which the search results are displayed, considering such factors as geo-targeting, as well as group and individual member demographics and metrics. The company parameter suggests that properties owned or controlled by a particular brand should be listed first.) This shows that lifetime value is communicated to a search engine as seen in fig. 4. The search engine determines search results and price points based on the user lifetime value.
wherein the user lifetime values are utilized by the search engine in conducting searches responsive to search requests from user devices (See para 0042- The engine then loads the consumer's lifetime value and event history. Based on each member's status and history, the club engine creates a personalized rules set and configures programs, discounts, price points, virtual currencies, payment options, and the like.) (See para 0082- The prioritization module refers to the order in which the search results are displayed, considering such factors as geo-targeting, as well as group and individual member demographics and metrics. The company parameter suggests that properties owned or controlled by a particular brand should be listed first.) The results as seen in figure 4 are based on based on the user lifetime values since the price points and discounts are based on this value. Fig, 4 shows a search results page for a search request.
Soon-Shiong, Price, Kannan, and Rowley are analogous art because they are from the same problem solving area of analyzing consumers with respect to making a purchase and all belong to G06Q30 classification. It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to have combined Soon-Shiong’s, Price’s, and Kannan’s invention by incorporating the method of Rowley because they could use the customization of webpages with respect to user lifetime values. This would help the arts of Soon-Shiong, Price, and Kannan better engage with customers which would lead to successful transactions. For example, this would help the art of Soon-Shiong and Kannan when customers are searching for products to purchase. Soon-Shiong and Kanan would be able to customize the websites to the customer’s preferences. Price would also be able to use customer data to accurately search for the perfect sales associate to deal with a customer.
In addition, another section of Kannan teaches
so as to increase the website's visibility in search engine results of the searches. (See para 0027- When consumer enters a date and city into a search box on a travel site, the system interrogates a number of predetermined internet sources, and runs the data through an algorithm. Part of the FMV algorithm may involve weighting some sites more heavily than other sites. One objective is to compute an objectively reasonable and verifiable FMV in order to support the system's value proposition to the member. The algorithm used to compute the hotel room FMV may be configured to prioritize first tier sites, and to ignore or give lower weight to second tier sites. In this context, first tier sites may include high traffic, high visibility sites, whereas second tier sites may include sites which the member is not likely to look to for price verification.) This shows the system increases visibility for first tier websites compared to lower tier ones.
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to have combined Kannan’s invention about increasing visibility of first tier websites with another section of Kannan about customer lifetime value because the system of Kannan can determine users having high lifetime values and pairing them with first tier websites to increase the likelihood of a sale. This would ensure continued engagement of users to keep a high lifetime value compared to users with a lower lifetime value.
Soon-Shiong, Price, Kannan, and Rowley are analogous art because they are from the same problem solving area of analyzing consumers with respect to making a purchase and all belong to G06Q30 classification. It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to have combined Soon-Shiong’s, Price’s, and Kannan’s invention by incorporating the method of Rowley because they could use the customization of webpages with respect to user lifetime values. This would help the arts of Soon-Shiong, Price, and Kannan better engage with customers which would lead to a successful transactions. For example, this would help the art of Soon-Shiong and Kannan when customers are searching for products to purchase. Soon-Shiong and Kanan would be able to customize the websites to the customer’s preferences. Price would be able to use customer data to accurately search for the perfect sales associate to deal with a customer.
Regarding claim 2 and similarly claims 9 and 16, Soon-Shiong, Price, Kannan, and Rowley teach the limitations of 1, 8, and 15, however Kannan further teaches
wherein determining the corresponding user lifetime value comprises aggregating a set of lifetime user values on a per user basis. (See para 0044- In an example scenario, a monetary value may be determined corresponding to each interaction channel that the customer has used for interacting with the enterprise and the CLV estimate may be computed by averaging or weighted averaging of the monetary values corresponding to the various interaction channels. For example, a monetary value corresponding to the web channel may be determined based on cumulative revenue or margin from all historic purchases, as well as aggregated weighted monetary value of all products viewed by the customer, ) This teaches aggregating user product values to determine the CLV.
Soon-Shiong, Price, Rowley and Kannan are analogous art because they are from the same problem solving area of analyzing consumers with respect to making a purchase and all belong to G06Q30 classification. It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to have combined Soon-Shiong’s, Price’s, and Rowley’s invention by incorporating the method of Kannan because Soon-Shiong would also be able to use lifetime values for the consumers when determining what kind of engagement points to use with the consumer. This makes the system of Soon-Shiong more sophisticated since it would provide more in-depth data about consumers. Price would also be able to use lifetime values for the customers when determining recommendations which kind of sales associate to deal with the customers. Picking the right sales associate would translate to successful sales. This makes the system of Price more sophisticated since it would provide more in-depth data about consumers. Rowley would also be able to use aggregation when determining a user lifetime value. This would let the user of Rowley see user history with respect to products they were interested in.
Regarding claim 4 and similarly claim 11, Soon-Shiong, Price, Kannan, and Rowley teach the limitations of 1 and 8, however Soon-Shiong further teaches
wherein the user information comprises a phone number, an email address, or a combination thereof. (See para 0047- potential consumers through the consumers' social network, emails, or other communication tools.) This teaches email.
Regarding claim 5 and similarly claim 12 and 18, Soon-Shiong, Price, Kannan, and Rowley teach the limitations of 1, 8, and 15, however Soon-Shiong further teaches
wherein the user-related features capture user behaviors in interacting with the website or with a partner website of the vehicle data system, wherein the user-related features comprise at least one of: a device category, a viewed page which identifies whether a user viewed a new vehicle page or a used vehicle page, a user source segment, a number of unique pages viewed, a number of sessions visited, a number of price report pages viewed via the website or the partner website, a number of leads sent, day of week of lead submission, or day of week of first visit. The clickstream data as taught in Soon-Shiong corresponds to interacting with a website. It compromises number of sessions as seen here (See para 0026- In another example, based on a click-stream history of the consumer that shows frequent visits to a vehicle manufacturer website or a dealer website) frequent visits corresponds to number of sessions.
Regarding claims 7 and similarly claim 14 and 20, Soon-Shiong, Price, Kannan, and Rowley teach the limitations of 1, 8, and 15, however Soon-Shiong further teaches
wherein the vehicle-related features comprise at least one of: a vehicle model segment or a vehicle type. (See para 0026- In another example, based on a click-stream history of the consumer that shows frequent visits to a vehicle manufacturer website or a dealer website) This shows vehicle type such as manufacturer of vehicle such as Lexus or Toyota.
Conclusion
The prior art made of record and not relied upon considered pertinent to Applicant’s disclosure.
Boss (US20180341898A1) who teaches test data.
Mourad (US20060080210A1) who teaches distance from dealers to customers.
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/MUSTAFA IQBAL/Primary Examiner, Art Unit 3625
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