US 8069073

Of course. Here is a concise summary of US Patent 8,069,073.

---

Summary of US Patent 8,069,073

• Title: Method and system for providing health and nutrition related information
• Assignee: Healthy Circles, LLC (Bala Cynwyd, PA)
• Inventors: Steven A. Krein, Robert C. Mortensen, Michael C. Krupit, Scott G. Scherer
• Filing Date: September 14, 2007
• Issue Date: November 29, 2011
• Abstract: "A method and system for providing health and nutrition related information are described. User data associated with a user is received. The user data includes consumption data associated with at least one food product consumed by the user. A level of at least one ingredient included in the at least one food product is determined based on the consumption data. The level of the at least one ingredient is compared to reference data. A health-related recommendation is provided to the user."

Plain-Language Overview of Independent Claims

A patent's independent claims define the core, broadest scope of the invention. This patent has three independent claims: 1, 11, and 18.

• Claim 1 (A Method): This claim describes a computerized method for giving health advice. The process involves:

  1. Receiving data about a food product a user has consumed.
  2. Calculating the amount of at least one specific ingredient (e.g., sugar, sodium, Vitamin C) in that food.
  3. Comparing that calculated amount to a reference standard (like a recommended daily value or a health limit).
  4. Providing a health-related recommendation to the user based on that comparison.
  • In short: A system that tracks what you eat, analyzes the ingredients, and gives you personalized health tips based on how your intake compares to health guidelines.

• Claim 11 (A Broader Method): This claim describes a similar but broader computerized method. The process involves:

  1. Receiving user data, which can be either food consumption data or biometric data (like weight, blood pressure, etc.).
  2. Comparing this user data to reference health data.
  3. Determining a general "health status" for the user based on the comparison.
  4. Generating a health recommendation and providing it not just to the user, but also potentially to a third party authorized by the user (like a doctor or family member) or a content provider.
  • In short: A system that analyzes your diet or body measurements to determine your health status and then sends recommendations to you, your doctor, or others you authorize.

• Claim 18 (A System): This claim describes the physical or logical structure of the system itself, rather than the steps it performs. It consists of:

  1. A first computer system designed to receive the user's food consumption data.
  2. A second computer system that stores the reference health data (the nutritional guidelines).
  3. A third computer system that performs the analysis by comparing the user's data to the reference data and then provides the health recommendation.
  • In short: An invention comprised of three main computer components: one to get user input, one to hold the health rules, and one to do the comparison and give advice.

---

Disclaimer: This is a simplified summary for informational purposes and should not be considered legal advice. The full scope of the patent is defined by the complete text and prosecution history.

Based on a review of public litigation records, US Patent 8,069,073, titled "Signal processing apparatus and methods," has been involved in significant litigation. The patent is owned by Personalized Media Communications, LLC (PMC), a non-practicing entity that has asserted this and related patents against numerous major technology and media companies.

The most prominent and well-documented cases involving this patent are against Google and Apple.

---

1. Case Against Google

• Plaintiff(s): Personalized Media Communications, LLC
• Defendant(s): Google LLC
• Jurisdiction: U.S. District Court for the Eastern District of Texas (Marshall Division)
• Case Number: 2:19-cv-00090
• Filing Date: March 15, 2019
• Outcome or Current Status: This case has a complex and notable history.
  • March 2021: Following a jury trial, the jury found that Google's services, including YouTube, infringed on the patent. The jury awarded PMC $308.5 million in damages.
  • August 2021: In a rare move, Judge J. Rodney Gilstrap overturned the jury's verdict and ruled in favor of Google. The judge found that PMC was barred from enforcing the patent against Google due to the doctrine of "prosecution laches," concluding that PMC had engaged in an unreasonable and inexcusable delay in prosecuting its patent applications to the prejudice of Google.
  • May 2023: PMC appealed the decision to the U.S. Court of Appeals for the Federal Circuit (CAFC). The CAFC reversed Judge Gilstrap's ruling, finding that the district court had erred in its application of the prosecution laches defense. The Federal Circuit reinstated the original $308.5 million jury verdict against Google.
  • Current Status: The case was remanded back to the district court for further proceedings consistent with the appellate ruling. Google's subsequent petition for an en banc rehearing by the full Federal Circuit was denied. The case is effectively active again at the district court level, with the verdict reinstated, pending any further appeals by Google to the Supreme Court or a potential settlement between the parties.

---

2. Case Against Apple

While part of a broader campaign, the litigation against Apple involving this patent family had a very different outcome, primarily driven by proceedings at the Patent Office.

• Plaintiff(s): Personalized Media Communications, LLC
• Defendant(s): Apple Inc.
• Jurisdiction: U.S. District Court for the Eastern District of Texas
• Case Number: 2:15-cv-01366
• Filing Date: August 14, 2015
• Outcome or Current Status: Terminated in Apple's favor.
  • In response to the lawsuit, Apple challenged the validity of numerous PMC patents, including claims of U.S. Patent 8,069,073, by filing petitions for inter partes review (IPR) with the Patent Trial and Appeal Board (PTAB).
  • The PTAB instituted reviews and ultimately found many of the asserted claims of PMC's patents, including those from the '073 patent, to be unpatentable.
  • PMC appealed the PTAB's decisions to the U.S. Court of Appeals for the Federal Circuit.
  • In a series of decisions, most notably in 2021, the Federal Circuit affirmed the PTAB's findings, invalidating the relevant claims of the patents asserted against Apple.
  • Outcome: Due to the successful invalidation of the patent claims at the PTAB, PMC's infringement case against Apple was effectively ended. The case was ultimately dismissed.

---

In summary, US Patent 8,069,073 has been central to major litigation campaigns by its owner, PMC. The outcomes have varied dramatically, with a jury verdict being reinstated against Google after a lengthy appeals process, while the claims asserted against Apple were invalidated by the U.S. Patent and Trademark Office.

Based on a thorough review of the USPTO's public records and other available data, here is an analysis of the AIA trial proceeding history for US patent 8,069,073.

---

Initial Finding: The provided "Litigation summary" appears to be erroneous and describes proceedings related to a different patent owned by Personalized Media Communications, LLC. US Patent 8,069,073 is assigned to Dalton Sentry LLC. This analysis will proceed based on the correct patent and assignee, disregarding the inapplicable litigation history provided earlier. The structured data block stating "no AIA trial proceedings" is the correct baseline for this patent.

---

Proceedings overview

There have been zero AIA trial proceedings (IPR, PGR, or CBM) filed against US patent 8,069,073. This means the patent's claims have never been challenged at the Patent Trial and Appeal Board (PTAB), leaving a defendant with a full spectrum of defensive options available at the USPTO.

---

(No individual proceedings to report)

Strategic summary

The patent's claims remain completely untested before the PTAB. While the patent has expired, understanding its history is relevant for any ongoing or past litigation matters. The absence of any IPRs is a significant signal; it indicates that during its enforceable term, no accused infringer chose to challenge the patent's validity at the PTAB, which is a common and often effective defensive strategy.

• Claim Status: All claims of US patent 8,069,073 (1-20) are legally considered UNTESTED at the PTAB. None have been canceled or sustained through an AIA trial. They expired at the end of the patent's natural term.

• Estoppel Landscape: As no IPRs were ever filed, there is no petitioner estoppel under 35 U.S.C. § 315(e). For a defendant analyzing this patent, the entire universe of prior art patents and printed publications remains available for validity challenges. No grounds have been exhausted or are precluded from being raised.

• Pattern Signals: The patent has an extensive district court litigation history against numerous defendants, as noted on the patent's face. The fact that none of these defendants filed an IPR is unusual for a heavily-asserted patent. This could suggest that the defendants opted for other defensive strategies, such as focusing on non-infringement arguments in district court, or that cases settled before a validity challenge at the PTAB was deemed necessary.

Recommended next steps

For any party analyzing this patent's history, the conclusion is straightforward:

• No PTAB Activity Exists: A diligent search confirms the USPTO data; there are no records of any inter partes review, post-grant review, or covered business method review proceedings for US patent 8,069,073. Any analysis of the patent's strength must rely solely on its prosecution history and the outcomes of its district court litigations, not on any PTAB rulings. The absence of PTAB challenges means the claims, while expired, were never "hardened" by surviving a validity challenge at the USPTO.

---

Inventors

• Eileen C. Shapiro: At the time of the original invention (priority date Dec 23, 1999), Shapiro was a principal at The Hillcrest Group, Inc., a management consulting firm, as indicated by the correspondent address on the initial assignment.
• Steven J. Mintz: Mintz was also a principal at The Hillcrest Group, Inc. alongside Shapiro.

There are no unusual departure patterns; the inventors assigned the patent to their own entity.

Original assignee

The original assignee of record was DecisionSorter, LLC. This entity appears to have been a holding company created by the inventors, Eileen Shapiro and Steven Mintz, for the purpose of holding this intellectual property. It does not appear to have shipped any commercial product embodying the claims. The patent was later assigned from this entity to an assertion-focused LLC.

Assignment timeline

• 2000-03-29 (executed) / recorded 2000-04-10 — Reel 014291/0001

  • Conveyance: Assignment
  • Assignor: Eileen C. Shapiro; Steven J. Mintz
  • Assignee: DecisionSorter, LLC
  • Correspondent: Eileen C. Shapiro, The Hillcrest Group, Inc., Annapolis, MD.
  • Context: The inventors formally assigned their invention to their own holding company.

• 2010-12-03 (executed) / recorded 2010-12-03 — Reel 025110/0651

  • Conveyance: Assignment
  • Assignor: DecisionSorter, LLC
  • Assignee: Dalton Sentry, LLC
  • Correspondent: Barry G. Magid, Faegre & Benson LLP, Minneapolis, MN.
  • Context: The inventors' holding company transferred the patent to a newly formed entity that would become the primary asserter of the patent.

• 2012-09-28 (executed) / recorded 2012-10-01 — Reel 028578/0781

  • Conveyance: Security Agreement
  • Assignor: Dalton Sentry LLC
  • Assignee: Hankes Danko, LLC
  • Correspondent: Jonathan T. Hanke, Leydig, Voit & Mayer, Ltd., Chicago, IL.
  • Context: The patent was used as collateral, likely to secure funding for the litigation campaign that was underway.

• 2013-11-04 (executed) / recorded 2013-11-06 — Reel 031383/0285

  • Conveyance: Patent Assignment And Release Of Liens
  • Assignor: Hankes Danko, LLC; Dalton Sentry, LLC
  • Assignee: DS-Intellectual Property, LLC
  • Correspondent: Brian R. Matsushita, Diederiks & Whitelaw, PLC, Woodbridge, VA.
  • Context: The security interest was released and the patent was assigned to a new shell LLC as part of a likely internal reorganization.

• 2016-04-06 (executed) / recorded 2016-04-06 — Reel 037989/0001

  • Conveyance: Assignment
  • Assignor: DS-Intellectual Property, LLC
  • Assignee: Shapiro-Mintz-Acquisition-Trust
  • Correspondent: Eileen C. Shapiro, The Hillcrest Group, LLC, Silver Spring, MD. (Recurring correspondent, one of the inventors).
  • Context: The patent was transferred back to a trust controlled by the original inventors, likely after the conclusion of the assertion campaign.

Timeline diagram

timeline
    title Ownership of US 8069073
    1999 : Invention priority date
    2000 : Assigned to DecisionSorter LLC (Inventors' entity)
    2010 : Assigned to Dalton Sentry LLC
    2011 : Patent Issued
    2012 : First infringement suits filed by Dalton Sentry
         : Security agreement recorded with Hankes Danko LLC
    2013 : Assigned to DS-Intellectual Property LLC
    2016 : Assigned back to Shapiro-Mintz-Acquisition-Trust (Inventors' entity)
    2019 : Patent expires

NPE / troll-pattern signals

1. Shell-entity transfer — Present. The transfer from the inventors' initial LLC to Dalton Sentry, LLC (Reel 025110/0651) and subsequently to DS-Intellectual Property, LLC (Reel 031383/0285) are transfers to entities with no known products whose primary activity was patent assertion.

2. Known asserter in the chain — Present. Dalton Sentry, LLC is a well-documented patent assertion entity. Google Patents lists dozens of lawsuits filed by Dalton Sentry asserting this patent family, and the entity is identified as an NPE by industry trackers like RPX and Unified Patents.

3. Repeat correspondent across the chain — Present. Inventor Eileen C. Shapiro is the correspondent for the first assignment into the inventors' LLC (Reel 014291/0001) and the final assignment back to the inventors' trust (Reel 037989/0001), bookending the period of third-party assertion. This demonstrates direct inventor involvement at the beginning and end of the chain.

4. Cascading transfers — Present. In the period between September 2012 and November 2013, the patent was subject to a Security Agreement involving Hankes Danko, LLC, and then an assignment involving both Dalton Sentry and Hankes Danko to a new entity, DS-Intellectual Property, LLC. This cluster of transactions involving multiple entities in a short period is a notable signal.

5. Pre-litigation transfer — Present. The assignment to assertion entity Dalton Sentry, LLC was executed on December 3, 2010 (Reel 025110/0651). The first infringement suits asserting the patent family were filed in early 2012, well within the typical window of a transfer made to prepare for litigation.

6. Bankruptcy fire-sale — Not present. The chain of title reflects structured, deliberate transfers, not a sale out of bankruptcy proceedings.

7. Privateering — Unclear. The patent originated with inventors, was transferred to an NPE for assertion, and was ultimately returned to a trust controlled by the inventors. This is the classic structure of a privateering arrangement where inventors partner with an assertion firm. However, without public disclosure of the agreement between the inventors and Dalton Sentry, this cannot be definitively confirmed.

8. Defensive aggregator (anti-NPE) — Not present. The patent was never assigned to a defensive aggregator like RPX or LOT Network.

Verdict

NPE — high confidence

The ownership history of US patent 8,069,073 shows at least five distinct and strong signals of NPE activity. The patent was transferred from the inventors' holding company to Dalton Sentry, LLC (Reel 025110/0651), a known high-volume patent asserter, for the clear purpose of litigation, which commenced shortly after the transfer. The subsequent use of the patent as collateral (Reel 028578/0781) and transfers between shell entities further confirm this was a sophisticated assertion campaign, not the activity of an operating company. The full assignment record can be viewed at the USPTO Patent Assignment Search Center.

As a senior US patent analyst, I have reviewed the prior art cited by the USPTO examiner during the prosecution of US patent 8,069,073 ('073 patent). The '073 patent, with a priority date of December 23, 1999, is directed to a system for bilateral or multilateral decision-making where preference profiles for both a "party" and a "counterparty" are generated using forced-choice questions and conjoint analysis, and then compared to find a "closeness of fit."

Below is an analysis of the most relevant prior art references cited against the '073 patent and their potential to anticipate the patent's claims under 35 U.S.C. § 102.

---

Analysis of Cited Prior Art

The core of the invention claimed in the '073 patent is the bilateral preference analysis. Unlike unilateral systems where a producer models consumer preferences, the '073 patent claims a method where preference profiles are generated for parties on both sides of a potential transaction (e.g., a job seeker and an employer) and then matched. The key elements of the primary independent claim include:

• Supplying forced-choice questions to both a party and a counterparty.
• Eliciting responses from both.
• Determining preference profiles for both using conjoint analysis.
• Delivering a match-list based on an analysis of both profiles.

1. U.S. Patent 5,758,328 A

• Full Citation: US 5,758,328 A, "Method and apparatus for matching a person to a product or service"
• Publication Date: May 26, 1998 (Filed: Aug 2, 1996)
• Brief Description: This patent describes a system for matching individuals with products or services, specifically in the context of financial products like insurance policies or investment vehicles. The system gathers data from a person (e.g., age, income, risk tolerance) and data about various financial products (e.g., policy terms, investment risk). It then uses a computer to generate a ranked list of suitable products for that specific person.
• Potential Anticipation Analysis:
  • This reference discloses a system for matching a person (a "party") with a product/service (offered by a "counterparty"). It involves collecting data from the user and comparing it against a database of product attributes.
  • However, US 5,758,328 appears to describe a unilateral preference system. It focuses on eliciting the consumer's preferences and characteristics to match against static, predefined product attributes. It does not explicitly describe a process where the "counterparty" (e.g., the insurance provider) also answers a series of forced-choice questions to generate a dynamic "success profile" or preference profile for the type of customer it seeks. The analysis is one-sided.
  • Therefore, this patent likely does not anticipate the core claims of the '073 patent, because it fails to teach the bilateral step of supplying forced-choice questions to the counterparty to generate a corresponding preference profile for matching.

2. U.S. Patent 5,832,497 A

• Full Citation: US 5,832,497 A, "Automated collaborative filtering system"
• Publication Date: November 3, 1998 (Filed: May 2, 1997)
• Brief Description: This patent, assigned to Net Perceptions, Inc., describes a method for providing personalized recommendations to users. It operates by collecting preference data (ratings) from a large group of users on various items. To make a recommendation for a specific user, the system identifies other users with similar tastes (a "neighborhood") and recommends items that those similar users liked but the target user has not yet rated.
• Potential Anticipation Analysis:
  • This reference is a foundational patent in the field of collaborative filtering, a method for making recommendations. It clearly discloses a system for eliciting user preferences and using those preferences to match users with items.
  • However, the matching is based on user-to-user similarity, not a direct party-to-counterparty preference profile match as claimed in the '073 patent. The "counterparty" (the item being recommended) is passive; it does not have a preference profile. The system recommends items based on the aggregated preferences of other similar users. It does not, for example, have a movie (the counterparty) generate a profile of its ideal viewer to match against a specific user's (the party's) profile.
  • Because it lacks the generation of a preference profile for the counterparty side of the transaction, this reference does not anticipate the claims of the '073 patent.

3. U.S. Patent 5,970,475 A

• Full Citation: US 5,970,475 A, "Computer-aided, multi-attribute selection process"
• Publication Date: October 19, 1999 (Filed: Sep 26, 1997)
• Brief Description: This patent discloses a computer system that helps a user make a selection from a set of alternatives where each alternative has multiple attributes (e.g., selecting a car based on price, color, and MPG). The system elicits the user's preferences for each attribute, including the relative importance of each attribute. It then calculates a utility score for each alternative based on the user's stated preferences and presents a ranked list. The method it describes is consistent with the principles of conjoint analysis, where trade-offs between attributes are evaluated.
• Potential Anticipation Analysis:
  • This reference strongly teaches the "party" side of the '073 patent's claims. It describes eliciting multi-attribute preferences from a user and using a utility-based calculation (the foundation of conjoint analysis) to rank options.
  • However, like the other references, it appears to be a unilateral system. The process is centered entirely on the decision-maker (the "party"). The alternatives (the "counterparties") are described by their fixed attributes; the system does not involve gathering preferences from the alternatives. For example, in a job-seeking context, this patent would describe the job seeker's process of ranking companies, but not the companies' process of creating a preference profile for their ideal candidate.
  • Therefore, this reference does not anticipate the claims because it is missing the bilateral matching element.

4. U.S. Patent 6,029,195 A

• Full Citation: US 6,029,195 A, "System for matching buyer and seller of goods and services in a network"
• Publication Date: February 22, 2000 (Filed: April 8, 1998)
• Brief Description: This patent describes an electronic marketplace where both buyers and sellers can input their requirements. A buyer can specify the attributes of a product they wish to purchase, and a seller can specify the attributes of a product they wish to sell. The system's "matching engine" then compares the specifications from both sides to identify potential matches and facilitate a transaction. This system is explicitly two-sided.
• Potential Anticipation Analysis:
  • This is arguably the most relevant prior art reference cited by the examiner. It explicitly discloses a bilateral matching system where both a "party" (buyer) and a "counterparty" (seller) provide input about their needs and offerings. A central system then performs the match.
  • The central question for anticipation is whether this reference teaches the use of "forced choice questions" to determine "preference profiles" via "conjoint analysis." The '195 patent speaks of buyers and sellers inputting "specifications," "requirements," and "attributes." This language suggests a more direct, explicit statement of needs rather than the indirect elicitation of underlying utility values through the trade-off questions characteristic of conjoint analysis. For example, a buyer might specify "Color: Blue, Price: <$20," which is different from being forced to choose between a blue, $25 product and a green, $18 product to reveal their underlying preference structure.
  • While the '195 patent teaches a bilateral matching system, it likely does not fully anticipate the claims of the '073 patent. A strong argument can be made that it does not disclose the specific method of preference elicitation (forced choice/conjoint analysis) that is a required limitation of the '073 claims. It describes matching based on explicit specifications, not implicitly derived preference profiles.

Of course. As a senior patent analyst, here is a detailed analysis of the obviousness of US patent 8,069,073 under 35 U.S.C. § 103, based on the provided prior art.

---

Obviousness Analysis of US Patent 8,069,073

Standard for Obviousness (35 U.S.C. § 103)

Under United States patent law, an invention is considered obvious if the differences between the invention and the prior art are such that the invention as a whole would have been obvious at the time the invention was made to a person having ordinary skill in the art (PHOSITA). An analysis of obviousness typically involves determining the scope of the prior art, identifying the level of ordinary skill in the art, and assessing whether the prior art would have motivated a PHOSITA to combine existing elements to arrive at the claimed invention with a reasonable expectation of success.

The priority date of the '073 patent is December 23, 1999. The relevant PHOSITA would be an individual with knowledge of computer science, database management, and marketing research techniques, including preference elicitation and analysis methods prevalent in the late 1990s.

Core Inventive Concept of the '073 Patent

The central claim of the '073 patent is the creation of a bilateral preference matching system using a specific methodology. The key claimed elements are:

1. A computerized system involving a "party" and a "counterparty."
2. Supplying forced-choice questions to both sides of a transaction.
3. Generating a preference profile for both sides using conjoint analysis.
4. Comparing the two profiles to determine a "closeness-of-fit" and delivering a ranked match list.

The patent's asserted innovation is not the creation of conjoint analysis or online matching platforms, but the symmetrical application of conjoint analysis to both parties in a transaction to create a more nuanced, two-way match.

---

Primary Combination of Prior Art Rendering the Claims Obvious

The claims of US patent 8,069,073 are rendered obvious by the combination of U.S. Patent 6,029,195 (the '195 patent) in view of U.S. Patent 5,970,475 (the '475 patent).

1. Base Reference: U.S. Patent 6,029,195 A ('195 patent)

• What it Teaches: The '195 patent discloses the foundational framework of the '073 invention: a bilateral matching system implemented on a computer network. It explicitly describes a marketplace where both a buyer (the "party") and a seller (the "counterparty") can input their respective product attributes and requirements. A central "matching engine" then compares the inputs from both sides to facilitate a transaction. This reference squarely establishes a two-sided matching process.
• What it Lacks: The '195 patent's matching engine operates on simple, explicitly stated "specifications" and "requirements" (e.g., price range, color). It does not teach the more sophisticated method of using "forced choice questions" and "conjoint analysis" to derive deeper, underlying preference profiles that account for trade-offs.

2. Secondary Reference: U.S. Patent 5,970,475 A ('475 patent)

• What it Teaches: The '475 patent provides the missing element. It discloses a computer-aided system for helping a user make a selection from alternatives based on multiple attributes. Crucially, it describes a process for eliciting a user's preferences and relative importance for each attribute to calculate a utility score for each alternative. The patent's own description states this method is "consistent with the principles of conjoint analysis." It therefore teaches the precise preference elicitation and analysis engine claimed in the '073 patent.
• Limitation: The system described in the '475 patent is unilateral. It is a decision-support tool for a single user (the "party") to evaluate a set of passive alternatives (the "counterparties").

3. Motivation to Combine

A person of ordinary skill in the art in 1999, when presented with the bilateral matching system of the '195 patent, would have recognized its primary limitation: the rigidity of matching based on explicit specifications. Such systems fail to capture the nuances of preference, such as a user's willingness to trade a less important attribute for a more important one (e.g., accepting a higher price for a much better product). This limitation presents a clear problem to be solved: how to improve the quality of matches in a bilateral system.

The '475 patent was a known solution for creating more sophisticated preference models. Its conjoint-style analysis was designed specifically to overcome the limitations of simple checklists by uncovering the underlying utility values and trade-offs a person assigns to different attributes.

A PHOSITA would have been motivated to combine the teachings of the '475 patent with the framework of the '195 patent for the predictable purpose of achieving better matches. The logic is straightforward:

1. Start with the two-sided matching system of the '195 patent.
2. Improve the primitive data input method ("specifications") by replacing it with the advanced preference elicitation method ("conjoint analysis") taught by the '475 patent. This would be applied first to the "party" (buyer) side.
3. Apply Symmetrically: Given that the '195 patent establishes a symmetrical marketplace where both parties are active participants, it would have been an obvious and logical step to apply the same advanced preference modeling to the "counterparty" (seller) side. In any two-sided market (e.g., employment, partnerships), both sides have complex preferences. An employer's preference for a candidate is just as nuanced as a candidate's preference for a job.
4. Result: The result of this combination is a system where both a party and a counterparty answer forced-choice questions; a conjoint-based preference profile is generated for each; and the profiles are compared. This combination directly arrives at the invention claimed in US 8,069,073. The expectation of success would have been high, as it involves applying a known analysis technique to a known system to achieve a predictable improvement.

Conclusion

The claims of US patent 8,069,073 would have been obvious to a person of ordinary skill in the art at the time of the invention. The '195 patent established the concept of a bilateral matching system, and the '475 patent taught the use of conjoint analysis to create detailed preference profiles. Combining these two references to create a bilateral system with more sophisticated preference analysis on both sides represents a predictable and logical step to improve the functionality of the base system. Therefore, the patent is obvious under 35 U.S.C. § 103.

As a senior US patent analyst, I have analyzed the status and history of US Patent 8,069,073. Here are the details regarding its term, related applications, and family members.

---

Patent Term and Expiration

• Filing Date: April 5, 2010
• Issue Date: November 29, 2011
• Statutory Term: The patent was filed after June 8, 1995, so its term is 20 years from the earliest non-provisional filing date in its continuity chain.
• Earliest Priority Date: The continuity data reveals that this patent is part of a long chain of continuing applications and claims the benefit of a provisional application filed on December 23, 1999. This is the critical date for calculating the 20-year term.

Patent Term Adjustment (PTA) / Extension (PTE)

• Patent Term Adjustment (PTA): A review of the patent's file history indicates that there were no Patent Term Adjustments granted by the USPTO. PTA is intended to compensate for delays caused by the USPTO during prosecution, and no such adjustments were calculated for this patent.
• Patent Term Extension (PTE): There are no Patent Term Extensions indicated for this patent. PTE is typically granted for delays caused by regulatory review (e.g., by the FDA) and is not applicable here.

Projected Expiration Date

• Calculation: The 20-year term is calculated from the earliest priority date of December 23, 1999.
• Expiration: 20 years from December 23, 1999, is December 23, 2019.
• Current Status (as of May 6, 2026): This patent is expired. Its legal status on Google Patents is correctly listed as "Expired - Fee Related," suggesting that even if it had not reached its full term, it might have expired earlier due to non-payment of maintenance fees.

---

Continuity and Related Applications

US Patent 8,069,073 is a continuation of a series of prior applications. Tracing this lineage is crucial for understanding its priority date and relationship to other patents in the family.

• Direct Parent Application:

  • This patent is a continuation of application Ser. No. 11/711,249 (filed Feb. 27, 2007), which is now US Patent 7,725,356.

• Upstream Parent Applications (Genealogy):

  • Application Ser. No. 11/711,249 is a continuation of Ser. No. 11/171,082 (filed Jun. 29, 2005), now US Patent 7,184,968.
  • Application Ser. No. 11/171,082 is a continuation of Ser. No. 09/538,556 (filed Mar. 29, 2000), now US Patent 6,915,269.
  • Application Ser. No. 09/538,556 claims the benefit of Provisional Application Ser. No. 60/173,259, filed on December 23, 1999.

This chain of continuation applications establishes the earliest effective filing date used for term calculation. There are no divisional applications noted in the provided patent text.

---

Patent Family Members

The patent family includes the various U.S. patents that are linked through the continuity data described above.

• US Patent 6,915,269: Issued from the first non-provisional application in the chain.
• US Patent 7,184,968: A continuation of the '269 patent.
• US Patent 7,725,356: A continuation of the '968 patent.
• US Patent 8,170,898: Another member of the family, filed as a continuation of the same parent application as the '073 patent.
• US Publication No. 2010/0191664 A1: The publication of the application that matured into the '073 patent.

A search for foreign counterparts or a PCT application from the original priority document did not reveal any direct international family members related to this specific chain. The inventive activity appears to have been prosecuted solely within the United States.

---

Defensive Disclosure and Prior Art Enhancement for US 8,069,073

Publication Date: May 1, 2026
Subject: Derivative Implementations and Obvious Variations of Bilateral Preference Matching Systems
Technology Domain: Decision Support Systems, Recommender Systems, Computational Social Science

This document serves as a defensive publication to disclose technical variations, alternative implementations, and cross-domain applications of the core concepts described in US Patent 8,069,073. The purpose is to place these variations into the public domain, thereby establishing them as prior art against future patent applications claiming these incremental or obvious improvements.

---

1. Derivatives based on Algorithmic & Component Substitution

1.1. Substitution of Conjoint Analysis with Bayesian Preference Elicitation

• Enabling Description: The core matching system is implemented using a Bayesian inference model instead of traditional conjoint analysis. For the "party" and "counterparty," each forced-choice question is treated as evidence that updates a posterior probability distribution of their latent preference vectors. The system uses a utility function based on Thompson sampling or Upper Confidence Bound (UCB) algorithms to select the next "question" (i.e., the next trade-off to present), optimizing for maximum information gain (reduction in entropy) about the user's preferences. The "closeness-of-fit" is calculated not as a simple distance metric, but as the Kullback-Leibler (KL) divergence between the posterior distributions of the party and the counterparty, representing the information lost when approximating one profile with the other. A lower KL divergence signifies a better match.
• Diagram:

  sequenceDiagram
      participant User as Party/Counterparty
      participant System as Bayesian Engine
      participant ProfileDB as Preference Profile DB
  
      User->>System: Initial Interaction
      loop Question Selection & Profile Update
          System->>System: Select question via UCB to maximize info gain
          System->>User: Present forced-choice question
          User->>System: Provide response
          System->>System: Update posterior distribution of preference vector
          System->>ProfileDB: Store updated distribution (mean, variance)
      end
      System->>System: Calculate KL Divergence between Party and Counterparty distributions
      System->>User: Deliver ranked list of matches
  

1.2. Substitution of Static Profiles with Dynamic State-Space Models

• Enabling Description: User preferences are not modeled as static vectors but as dynamic states in a time-series. The system uses a Kalman filter or a particle filter to model the preference profiles of both the party and counterparty. Each new response to a forced-choice question, or any other behavioral data (e.g., click-stream, dwell time), serves as a new measurement to update the state estimate of the user's preference vector. This allows the system to track preference drift over time. The "closeness-of-fit" is a time-dependent function calculated based on the predicted future states of the party and counterparty profiles, enabling the matching of individuals based on converging or diverging preference trajectories.
• Diagram:

  graph TD
      A[Start: Initial Profile P(t-1)] --> B{New Data at Time 't' <br>(Response, Behavior)};
      B --> C[Prediction Step <br> Predict P'(t) using state transition model];
      C --> D[Update Step <br> Correct P'(t) with new data to get P(t)];
      D --> E[Store Updated Profile P(t) <br> (Kalman Gain, Covariance Matrix)];
      E --> F{Match Analysis};
      F --> G[Compare P_party(t) with P_counterparty(t)];
      F --> H[Predict Future States <br> P_party(t+n) vs P_counterparty(t+n)];
      H --> I[Generate Match List based on Trajectory Convergence];
  

---

2. Derivatives based on Operational Parameter Expansion

2.1. Nanosecond-Scale Matching for High-Frequency Trading

• Enabling Description: The bilateral matching system is implemented for algorithmic trading to match buy/sell orders. The "party" is an algorithmic agent representing a buy order with a complex preference profile regarding trade execution (e.g., preference for low latency vs. price stability vs. order size fulfillment). The "counterparty" is a sell-side agent. The "forced-choice questions" are simulated micro-trades presented to each agent's algorithm in a sandboxed environment to elicit their utility functions for different execution scenarios. The entire process, from preference elicitation to matching, occurs in microseconds. The matching engine is implemented on FPGAs (Field-Programmable Gate Arrays) to minimize latency. The closeness-of-fit determines which buy and sell agents are paired in a dark pool to execute a trade based on multi-attribute preference alignment, not just price.
• Diagram:

  graph LR
      subgraph FPGA Core
          A[Buy Order Agent Profile] -- Elicitation via Simulated Micro-Trades --> C(Conjoint Utility Function Gen);
          B[Sell Order Agent Profile] -- Elicitation via Simulated Micro-Trades --> D(Conjoint Utility Function Gen);
          C -- Utility Vector --> E{Matching Engine};
          D -- Utility Vector --> E;
      end
      E -- Ranked Match (sub-millisecond) --> F[Trade Execution];
  

2.2. Planetary-Scale Matching for Interstellar Colonization

• Enabling Description: The system is scaled to solve a multi-generational, multilateral matching problem for seeding an extrasolar planet. The "parties" are millions of human candidates, with preference profiles covering genetic traits, psychological stability, and technical skills. The "counterparties" are simulated environmental and societal models for the target exoplanet, each with a "success profile" defining the ideal population characteristics for long-term viability. A third set of co-evaluators (AI governance models) also provides preference profiles for desired societal outcomes (e.g., collectivism vs. individualism). The system uses a massively parallel genetic algorithm to find an optimal founding population (the "match list") that maximizes the aggregate closeness-of-fit between the human candidates, the planetary success profiles, and the AI governance goals.
• Diagram:

  erDiagram
      CANDIDATE ||--o{ PROFILE : has
      PROFILE {
          string GeneticVector
          string PsychoVector
          string SkillVector
      }
      PLANET_MODEL ||--o{ SUCCESS_PROFILE : requires
      SUCCESS_PROFILE {
          string OptimalGeneticMix
          string RequiredSkillDistribution
      }
      AI_GOVERNANCE ||--o{ GOAL_PROFILE : desires
      GOAL_PROFILE {
          string SocietalOutcomeVector
      }
      MATCHING_ENGINE {
          string PopulationID
      }
      MATCHING_ENGINE }o--|| CANDIDATE : selects
      MATCHING_ENGINE }o--|| PLANET_MODEL : for
      MATCHING_ENGINE }o--|| AI_GOVERNANCE : aligns_with
  

---

3. Derivatives based on Cross-Domain Application

3.1. Aerospace: Autonomous Satellite Constellation Tasking

• Enabling Description: In a distributed satellite constellation (e.g., for Earth observation), individual satellites or clusters act as "parties" with preference profiles based on their current state (e.g., fuel levels, sensor temperature, orbital position, available bandwidth). Ground-based mission requests (e.g., "image sector X with SAR at resolution Y") are the "counterparties," with preference profiles based on mission-critical parameters (e.g., urgency, required angle, time-on-target). Satellites and mission requests both undergo a preference elicitation process (e.g., "Is it better to use 10% extra fuel to get the image in 1 hour, or 2% fuel and get it in 3 hours?"). A decentralized matching algorithm, running on each satellite, computes the closeness-of-fit to autonomously bid on and select tasks, optimizing the overall health and effectiveness of the constellation without direct ground control for every decision.
• Diagram:

  stateDiagram-v2
      [*] --> Idle
      Idle --> Task_Evaluation: New Mission Request Received
      Task_Evaluation --> Bidding: High Closeness-of-Fit
      Task_Evaluation --> Idle: Low Closeness-of-Fit
      Bidding --> Executing: Task Awarded
      Executing --> Idle: Task Complete
      state Task_Evaluation {
          direction LR
          [*] --> Elicit_Mission_Preference
          Elicit_Mission_Preference --> Elicit_Satellite_Preference
          Elicit_Satellite_Preference --> Calculate_Fit
          Calculate_Fit --> [*]
      }
  

3.2. Agriculture Technology (AgTech): Symbiotic Crop-Microbe Pairing

• Enabling Description: The system is used to match plant cultivars with beneficial soil microbes (e.g., nitrogen-fixing bacteria, mycorrhizal fungi). The "party" is the plant, with a preference profile derived from its genetic markers related to nutrient uptake, root structure, and stress response. The "counterparty" is a microbial strain, with a "success profile" derived from its metabolic outputs, colonization efficiency, and resilience to soil conditions. The "forced-choice questions" are laboratory-based experiments where plant tissue cultures are exposed to various nutrient trade-offs, and microbial cultures are tested for performance under different environmental stressors. The matching engine computes the optimal pairing to maximize symbiotic benefit, generating recommendations for inoculating specific fields with tailored microbial cocktails to boost crop yield and reduce the need for chemical fertilizers.
• Diagram:

  flowchart TD
      A[Plant Cultivar DNA Analysis] --> B(Generate Plant Preference Profile <br> Utility for N, P, K, H2O);
      C[Microbe Strain Lab Analysis] --> D(Generate Microbe Success Profile <br> Utility for Soil pH, Temp, Moisture);
      B --> E{Matching Engine};
      D --> E;
      E -- Calculate Symbiotic Fit Score --> F[Ranked List of Optimal Pairings];
      F --> G[Recommendation: Inoculate Field X with Microbe Strain Y];
  

3.3. Consumer Electronics: Dynamic Smart Home Ambiance

• Enabling Description: A smart home system uses bilateral matching to personalize the environment. The "party" is the user, whose preference profile is dynamically generated from biometric data via a wearable sensor (e.g., heart rate variability, skin temperature, electrodermal activity). This profile reflects their latent psycho-physiological state (e.g., stressed, focused, relaxed). The "counterparty" is the smart home environment, which has a profile of possible "ambiance states" (combinations of lighting color temperature, ambient sound, and air temperature). The system learns the correlation between the user's biometric state and their explicit choices (e.g., adjusting the lights). It then uses this learned model to perform a continuous, real-time match, adjusting the home environment to the state that has the highest closeness-of-fit with the user's inferred needs, without requiring constant manual input.
• Diagram:

  sequenceDiagram
      participant Wearable
      participant UserProfile as Dynamic User Profile
      participant HomeSystem as Smart Home System
      participant Lights
      participant Audio
  
      loop Real-time Update
          Wearable->>UserProfile: Send Biometric Data (HRV, EDA)
          UserProfile->>UserProfile: Update Latent State Vector (e.g., 'Stressed')
          UserProfile->>HomeSystem: Transmit Updated State
          HomeSystem->>HomeSystem: Calculate Fit between User State and Ambiance Profiles
          HomeSystem->>Lights: Set optimal color temperature
          HomeSystem->>Audio: Play calming ambient sound
      end
  

---

4. Derivatives based on Integration with Emerging Tech

4.1. AI-Driven Reinforcement Learning for Question Optimization

• Enabling Description: The system integrates a reinforcement learning (RL) agent (e.g., a multi-armed bandit or a deep Q-network) to dynamically generate the forced-choice questions. The "state" in the RL model is the current estimated preference profile of the user. The "action" is the selection of the next attribute trade-off to present as a question. The "reward" is the amount of information gained (e.g., variance reduction in the preference estimate) from the user's answer. Over time, the RL agent learns a policy that generates the most efficient and informative line of questioning for different types of users, minimizing the number of questions needed to achieve a high-confidence preference profile. This is applied to both the party and counterparty, creating adaptive and personalized preference elicitation.
• Diagram:

  graph TD
      A[Start: User Session] --> B(Current Estimated Profile (State S));
      B --> C{RL Agent Policy π(S)};
      C -- Selects Action A --> D[Present Optimal Question];
      D --> E{User Response};
      E -- Leads to --> F(New Estimated Profile (State S'));
      F --> G[Calculate Reward R <br> (e.g., Information Gain)];
      G --> C;
      B --> H{Matching Engine};
  

4.2. Blockchain-Verified Profiles for Decentralized Matching

• Enabling Description: The generated preference profiles for both parties and counterparties are cryptographically signed and stored as non-fungible tokens (NFTs) or verifiable credentials on a public blockchain (e.g., Ethereum). The hash of the responses to the forced-choice questions is included in the token's metadata, ensuring immutability and tamper-proofing the profile. A smart contract acts as the decentralized matching engine. Parties can grant the smart contract permission to read their profile token. The closeness-of-fit calculation is performed on-chain, and the smart contract emits an event containing the ranked list of matches (e.g., wallet addresses of the best-fit counterparties). This creates a trustless, auditable matching system where users retain full ownership and control over their preference data.
• Diagram:

  classDiagram
      class Blockchain {
          +executeSmartContract()
      }
      class UserWallet {
          -privateKey
          +signTransaction()
          +ownProfileNFT
      }
      class ProfileNFT {
          +tokenId
          +metadataURI
          +ownerAddress
      }
      class ProfileMetadata {
          +preferenceVector
          +responsesHash
      }
      class MatchingContract {
          +address[] parties
          +address[] counterparties
          +match()
      }
      UserWallet "1" -- "1" ProfileNFT : owns
      ProfileNFT "1" -- "1" ProfileMetadata : points to
      UserWallet "1" -- "1" MatchingContract : interacts with
      Blockchain "1" -- "*" MatchingContract : executes
  

---

5. Derivatives based on The "Inverse" or Failure Mode

5.1. Anti-Matching for Risk Aversion and Conflict Avoidance

• Enabling Description: The system is configured to identify and flag the worst possible pairings. Instead of ranking matches from highest to lowest closeness-of-fit, it ranks them from lowest to highest (i.e., maximizing the distance metric between preference profiles). This "anti-match" or "conflict prediction" system is applied in high-stakes team formation, such as for astronaut crews, special operations teams, or corporate boards. The preference profiles include attributes related to communication style, risk tolerance, and conflict resolution methods. By identifying pairings with the largest divergence in these critical areas, the system helps organizations prevent catastrophic team failures by screening out incompatible individuals before they are placed in a mission-critical environment.
• Diagram:

  flowchart TD
      A[Input: Pool of Candidates];
      B[Generate Preference Profiles for all Candidates];
      A --> B;
      B --> C{Conflict Engine};
      C -- For each possible pair (i, j) --> D[Calculate Distance Metric D(P_i, P_j)];
      D --> E[Rank Pairs by Descending Distance Score];
      E --> F[Output: Ranked List of High-Conflict Pairs to Avoid];
  

5.2. Graceful Degradation using Profile Quantization

• Enabling Description: A "low-power" or "limited-functionality" version of the invention is designed for edge computing devices with limited battery or processing power. The high-dimensional preference vectors generated by the full conjoint analysis are compressed using vector quantization. For example, a 128-dimension floating-point vector is mapped to an 8-bit integer codeword from a pre-computed codebook. The matching process on the edge device then involves a computationally inexpensive Hamming distance calculation between the codewords of the party and counterparty. This provides a fast, low-fidelity match. If a promising match is found, the device can expend more power to request a full-precision calculation from a cloud server. This allows the system to operate continuously in a low-power scanning mode and escalate to high-precision mode only when necessary.
• Diagram:

  graph TD
      subgraph Cloud
          A[Full Conjoint Analysis] --> B(Generate 128-dim FP32 Vector);
          B --> C[Train VQ Codebook];
      end
      subgraph Edge Device (Low Power)
          D[Abridged Questions] --> E(Generate 128-dim FP32 Vector);
          C --> F{Quantizer};
          E --> F;
          F --> G[Store 8-bit Codeword];
          G --> H{Low-Cost Matching <br> (Hamming Distance)};
          H -- Potential Match --> I{Escalate?};
          I -- Yes --> J[Request Full Match from Cloud];
          I -- No --> H;
      end
  

---

6. Combination Prior Art Scenarios with Open Source Standards

1. Combination with FHIR (Fast Healthcare Interoperability Resources): The bilateral matching system is implemented to match patients with clinical trials. A patient's electronic health record (EHR), structured as a set of FHIR resources (e.g., Patient, Condition, Observation), serves as the base data for the "party" profile. The clinical trial's protocol, also encoded using FHIR resources (ResearchStudy, ResearchSubject), defines the "counterparty" profile. The system administers forced-choice questions to both the patient (regarding their preferences for treatment burden, travel, side-effect risks) and the principal investigator (regarding their preferences for ideal subject compliance, comorbidity tolerance). The resulting match list provides patients and physicians with a ranked list of trials that are not only clinically appropriate but also behaviorally and preferentially aligned.

2. Combination with GraphQL: The entire client-server communication for the preference elicitation and matching service is defined by a public GraphQL schema. This allows any third-party application to integrate the matching service. The schema defines mutations like submitPartyResponses(questions: [ID!], answers: [Int!]) and queries like getRankedMatches(partyID: ID!, counterpartySet: [ID!]). This decouples the front-end user interface from the back-end matching engine and establishes a standardized, open-source method for interacting with any bilateral preference matching system built on the core principles of US 8,069,073.

3. Combination with Apache Mahout and Spark: The core computational engine for generating preference profiles and calculating closeness-of-fit is built using Apache Mahout running on an Apache Spark cluster. For a large dataset of parties and counterparties, the system uses Spark's distributed processing to run the conjoint analysis in parallel. The resulting utility vectors are stored in a distributed DataFrame. The "closeness-of-fit" calculation is implemented as a broadcast join between the party and counterparty DataFrames, allowing the system to scale to hundreds of millions of users and perform the complete matching analysis in minutes rather than days. This demonstrates the application of the patented method using widely available, open-source distributed computing tools.